i0008-beh-data
Overview
Init
Data
Code
ifd0 <- "data/i0001-src"
ofd0 <- "data/i0002-out"
dir.create(ofd0, showWarnings=FALSE, recursive=TRUE)
ifn0 <- "jgnb-df9v.csv"
suppressWarnings(rm(list=ls(pattern="^df")))
df0 <- readr::read_csv(
file = file.path(ifd0, ifn0),
show_col_types = FALSE,
col_types = list(
Sub = col_factor(),
Ses = col_factor(),
Run = col_factor(),
Chrono = col_factor(),
Condit = col_factor(),
Gender = col_factor(),
Education = col_factor(),
TP = col_integer(),
FP = col_integer(),
TN = col_integer(),
FN = col_integer(),
NiiAcqTime = col_character(), ## col_time(format = ""),
NiiAcqTIME = col_character(),
LogFileTime = col_character(),
.default = col_guess()),
) %>%
dplyr::filter(Run %in% c("01", "02", "03")) %>%
identity()
df0 %>% dim()[1] 900 74Factor Levels
Code
df2 <- df0 %>%
mutate(
Ses = factor(Ses, levels=c("Morning", "Evening")),
Run = factor(Run, levels=c("02", "01", "03")),
Chrono = factor(Chrono, levels = c("Morning", "Evening")),
Condit = factor(Condit, levels = c("Congruent", "Incongruent")),
) %>%
mutate(
NumBack = as.integer(NumBack),
NumBack = paste0("n", sprintf("%02d", NumBack)),
NumBack = factor(NumBack, levels = c("n02", "n01", "n03")),
) %>%
identity()
df0 %>% dim()[1] 900 74[1] 900 74Models
Code
REML <- TRUE
control <- lme4::lmerControl(optimizer = "Nelder_Mead")
fit.RT = lmerTest::lmer(formula=RT ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.TP = lmerTest::lmer(formula=TP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.FP = lmerTest::lmer(formula=FP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.TN = lmerTest::lmer(formula=TN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.FN = lmerTest::lmer(formula=FN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.Accur = lmerTest::lmer(formula=Accur ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.Sensi = lmerTest::lmer(formula=Sensi ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.Speci = lmerTest::lmer(formula=Speci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.Preci = lmerTest::lmer(formula=Preci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.FPR = lmerTest::lmer(formula=FPR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.FNR = lmerTest::lmer(formula=FNR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.FDR = lmerTest::lmer(formula=FDR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.NPV = lmerTest::lmer(formula=NPV ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.FOR = lmerTest::lmer(formula=FOR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)Save Models
Code
readr::write_rds(fit.RT, file.path(ofd0, "fit.RT.rds"))
readr::write_rds(fit.TP, file.path(ofd0, "fit.TP.rds"))
readr::write_rds(fit.FP, file.path(ofd0, "fit.FP.rds"))
readr::write_rds(fit.TN, file.path(ofd0, "fit.TN.rds"))
readr::write_rds(fit.FN, file.path(ofd0, "fit.FN.rds"))
readr::write_rds(fit.Accur, file.path(ofd0, "fit.Accur.rds"))
readr::write_rds(fit.Sensi, file.path(ofd0, "fit.Sensi.rds"))
readr::write_rds(fit.Speci, file.path(ofd0, "fit.Speci.rds"))
readr::write_rds(fit.Preci, file.path(ofd0, "fit.Preci.rds"))
readr::write_rds(fit.FPR, file.path(ofd0, "fit.FPR.rds"))
readr::write_rds(fit.FNR, file.path(ofd0, "fit.FNR.rds"))
readr::write_rds(fit.FDR, file.path(ofd0, "fit.FDR.rds"))
readr::write_rds(fit.NPV, file.path(ofd0, "fit.NPV.rds"))
readr::write_rds(fit.FOR, file.path(ofd0, "fit.FOR.rds"))Read Models
Code
fit.RT <- readr::read_rds(file.path(ofd0, "fit.RT.rds"))
fit.TP <- readr::read_rds(file.path(ofd0, "fit.TP.rds"))
fit.FP <- readr::read_rds(file.path(ofd0, "fit.FP.rds"))
fit.TN <- readr::read_rds(file.path(ofd0, "fit.TN.rds"))
fit.FN <- readr::read_rds(file.path(ofd0, "fit.FN.rds"))
fit.Accur <- readr::read_rds(file.path(ofd0, "fit.Accur.rds"))
fit.Sensi <- readr::read_rds(file.path(ofd0, "fit.Sensi.rds"))
fit.Speci <- readr::read_rds(file.path(ofd0, "fit.Speci.rds"))
fit.Preci <- readr::read_rds(file.path(ofd0, "fit.Preci.rds"))
fit.FPR <- readr::read_rds(file.path(ofd0, "fit.FPR.rds"))
fit.FNR <- readr::read_rds(file.path(ofd0, "fit.FNR.rds"))
fit.FDR <- readr::read_rds(file.path(ofd0, "fit.FDR.rds"))
fit.NPV <- readr::read_rds(file.path(ofd0, "fit.NPV.rds"))
fit.FOR <- readr::read_rds(file.path(ofd0, "fit.FOR.rds"))Tabulate Models
Code
sjPlot::tab_model(
fit.RT,
fit.TP, fit.FP, fit.TN, fit.FN,
fit.Accur,
fit.Sensi,
fit.Speci,
fit.Preci,
fit.FPR,
fit.FNR,
fit.FDR,
fit.NPV,
fit.FOR,
show.reflvl = FALSE,
show.intercept = TRUE,
show.p = FALSE,
wrap.labels = 888,
p.style = "numeric_stars",
# file = file.path(ofd0, "all-models-i0002-ALL.html"),
dv.labels = c(
"fit.RT",
"fit.TP", "fit.FP", "fit.TN", "fit.FN",
"fit.Accur",
"fit.Sensi",
"fit.Speci",
"fit.Preci",
"fit.FPR",
"fit.FNR",
"fit.FDR",
"fit.NPV",
"fit.FOR"))| fit.RT | fit.TP | fit.FP | fit.TN | fit.FN | fit.Accur | fit.Sensi | fit.Speci | fit.Preci | fit.FPR | fit.FNR | fit.FDR | fit.NPV | fit.FOR | |||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | Estimates | CI | Estimates | CI | Estimates | CI | Estimates | CI | Estimates | CI | Estimates | CI | Estimates | CI | Estimates | CI | Estimates | CI | Estimates | CI | Estimates | CI | Estimates | CI | Estimates | CI |
| (Intercept) | 0.69 *** | 0.62 – 0.77 | 5.52 *** | 5.11 – 5.93 | 0.61 ** | 0.20 – 1.02 | 17.64 *** | 16.85 – 18.42 | 0.54 ** | 0.17 – 0.91 | 0.95 *** | 0.93 – 0.98 | 0.91 *** | 0.85 – 0.97 | 0.97 *** | 0.94 – 0.99 | 0.91 *** | 0.85 – 0.96 | 0.03 ** | 0.01 – 0.06 | 0.09 ** | 0.03 – 0.15 | 0.09 *** | 0.04 – 0.15 | 0.97 *** | 0.95 – 0.99 | 0.03 ** | 0.01 – 0.05 |
| Ses [Evening] | -0.02 | -0.04 – 0.00 | 0.12 | -0.01 – 0.25 | 0.09 | -0.05 – 0.22 | 0.29 ** | 0.08 – 0.50 | 0.00 | -0.11 – 0.12 | -0.00 | -0.01 – 0.01 | 0.00 | -0.02 – 0.02 | -0.00 | -0.01 – 0.00 | -0.00 | -0.02 – 0.01 | 0.00 | -0.00 – 0.01 | -0.00 | -0.02 – 0.02 | 0.00 | -0.01 – 0.02 | -0.00 | -0.01 – 0.01 | 0.00 | -0.01 – 0.01 |
| Chrono [Evening] | 0.09 * | 0.02 – 0.17 | 0.08 | -0.27 – 0.43 | 0.04 | -0.30 – 0.38 | 0.52 | -0.23 – 1.27 | 0.10 | -0.21 – 0.41 | -0.00 | -0.03 – 0.02 | -0.01 | -0.06 – 0.04 | -0.00 | -0.02 – 0.02 | -0.01 | -0.06 – 0.04 | 0.00 | -0.02 – 0.02 | 0.01 | -0.04 – 0.06 | 0.01 | -0.04 – 0.06 | -0.00 | -0.02 – 0.01 | 0.00 | -0.01 – 0.02 |
| Run [01] | 0.02 | -0.01 – 0.04 | -0.20 * | -0.36 – -0.05 | 0.10 | -0.06 – 0.26 | -0.10 | -0.36 – 0.16 | 0.20 ** | 0.06 – 0.35 | -0.01 * | -0.02 – -0.00 | -0.03 ** | -0.06 – -0.01 | -0.01 | -0.01 – 0.00 | -0.02 | -0.04 – 0.00 | 0.01 | -0.00 – 0.01 | 0.03 ** | 0.01 – 0.06 | 0.02 | -0.00 – 0.04 | -0.01 ** | -0.02 – -0.00 | 0.01 ** | 0.00 – 0.02 |
| Run [03] | 0.00 | -0.02 – 0.03 | -0.01 | -0.17 – 0.15 | -0.05 | -0.21 – 0.11 | 0.05 | -0.20 – 0.31 | 0.01 | -0.13 – 0.15 | 0.00 | -0.01 – 0.01 | -0.00 | -0.03 – 0.02 | 0.00 | -0.01 – 0.01 | 0.00 | -0.02 – 0.02 | -0.00 | -0.01 – 0.01 | 0.00 | -0.02 – 0.03 | -0.00 | -0.02 – 0.02 | 0.00 | -0.01 – 0.01 | -0.00 | -0.01 – 0.01 |
| Condit [Incongruent] | 0.02 | -0.01 – 0.06 | 0.26 * | 0.05 – 0.47 | -0.01 | -0.22 – 0.21 | 1.05 *** | 0.70 – 1.40 | 0.07 | -0.12 – 0.26 | 0.00 | -0.01 – 0.01 | -0.01 | -0.04 – 0.03 | 0.00 | -0.01 – 0.01 | 0.00 | -0.03 – 0.03 | -0.00 | -0.01 – 0.01 | 0.01 | -0.03 – 0.04 | -0.00 | -0.03 – 0.03 | -0.00 | -0.01 – 0.01 | 0.00 | -0.01 – 0.01 |
| NumBack [n01] | -0.15 *** | -0.17 – -0.12 | 0.60 *** | 0.44 – 0.76 | -0.71 *** | -0.87 – -0.55 | 0.71 *** | 0.45 – 0.96 | -0.60 *** | -0.74 – -0.46 | 0.05 *** | 0.04 – 0.07 | 0.10 *** | 0.08 – 0.13 | 0.04 *** | 0.03 – 0.05 | 0.11 *** | 0.09 – 0.13 | -0.04 *** | -0.05 – -0.03 | -0.10 *** | -0.13 – -0.08 | -0.11 *** | -0.13 – -0.09 | 0.03 *** | 0.02 – 0.04 | -0.03 *** | -0.04 – -0.02 |
| NumBack [n03] | 0.13 *** | 0.11 – 0.16 | -1.06 *** | -1.22 – -0.91 | 0.62 *** | 0.46 – 0.78 | -0.62 *** | -0.88 – -0.37 | 1.06 *** | 0.92 – 1.21 | -0.07 *** | -0.08 – -0.06 | -0.18 *** | -0.20 – -0.15 | -0.03 *** | -0.04 – -0.03 | -0.12 *** | -0.14 – -0.10 | 0.03 *** | 0.03 – 0.04 | 0.18 *** | 0.15 – 0.20 | 0.12 *** | 0.10 – 0.14 | -0.06 *** | -0.06 – -0.05 | 0.06 *** | 0.05 – 0.06 |
| KSS | -0.01 | -0.02 – 0.01 | -0.16 *** | -0.25 – -0.07 | 0.08 | -0.01 – 0.17 | -0.43 *** | -0.58 – -0.28 | 0.05 | -0.03 – 0.13 | -0.01 * | -0.01 – -0.00 | -0.01 | -0.02 – 0.00 | -0.00 | -0.01 – 0.00 | -0.01 | -0.02 – 0.00 | 0.00 | -0.00 – 0.01 | 0.01 | -0.00 – 0.02 | 0.01 | -0.00 – 0.02 | -0.00 | -0.01 – 0.00 | 0.00 | -0.00 – 0.01 |
| Gender [m] | -0.03 | -0.11 – 0.04 | 0.39 * | 0.04 – 0.74 | -0.21 | -0.55 – 0.12 | 0.40 | -0.35 – 1.15 | -0.32 * | -0.63 – -0.02 | 0.02 * | 0.00 – 0.05 | 0.06 * | 0.00 – 0.11 | 0.01 | -0.01 – 0.03 | 0.04 | -0.01 – 0.08 | -0.01 | -0.03 – 0.01 | -0.06 * | -0.11 – -0.00 | -0.04 | -0.08 – 0.01 | 0.02 * | 0.00 – 0.03 | -0.02 * | -0.03 – -0.00 |
| Random Effects | ||||||||||||||||||||||||||||
| σ2 | 0.02 | 0.95 | 1.00 | 2.55 | 0.80 | 0.00 | 0.02 | 0.00 | 0.02 | 0.00 | 0.02 | 0.02 | 0.00 | 0.00 | ||||||||||||||
| τ00 | 0.02 Sub | 0.34 Sub | 0.31 Sub | 1.65 Sub | 0.25 Sub | 0.00 Sub | 0.01 Sub | 0.00 Sub | 0.01 Sub | 0.00 Sub | 0.01 Sub | 0.01 Sub | 0.00 Sub | 0.00 Sub | ||||||||||||||
| ICC | 0.40 | 0.27 | 0.24 | 0.39 | 0.24 | 0.27 | 0.23 | 0.23 | 0.25 | 0.23 | 0.23 | 0.25 | 0.24 | 0.24 | ||||||||||||||
| N | 51 Sub | 51 Sub | 51 Sub | 51 Sub | 51 Sub | 51 Sub | 51 Sub | 51 Sub | 51 Sub | 51 Sub | 51 Sub | 51 Sub | 51 Sub | 51 Sub | ||||||||||||||
| Observations | 898 | 900 | 900 | 900 | 900 | 891 | 891 | 891 | 889 | 891 | 891 | 889 | 891 | 891 | ||||||||||||||
| Marginal R2 / Conditional R2 | 0.272 / 0.561 | 0.299 / 0.485 | 0.198 / 0.387 | 0.122 / 0.466 | 0.329 / 0.490 | 0.355 / 0.527 | 0.335 / 0.491 | 0.201 / 0.387 | 0.291 / 0.467 | 0.201 / 0.387 | 0.335 / 0.491 | 0.291 / 0.467 | 0.344 / 0.502 | 0.344 / 0.502 | ||||||||||||||
| * p<0.05 ** p<0.01 *** p<0.001 | ||||||||||||||||||||||||||||
Plot Models
Code
custom_palette <- c(brewer.pal(9, "Set1"), brewer.pal(5, "Set2"))
line0h <- ggplot2::geom_hline(yintercept = 0, linetype = "dashed", color = "black", lwd = 0.125)
gg88 <- sjPlot::plot_models(
fit.RT,
fit.TP, fit.FP, fit.TN, fit.FN,
fit.Accur,
fit.Sensi,
fit.Speci,
fit.Preci,
fit.FPR,
fit.FNR,
fit.FDR,
fit.NPV,
fit.FOR,
m.labels = c(
"fit.RT",
"fit.TP", "fit.FP", "fit.TN", "fit.FN",
"fit.Accur",
"fit.Sensi",
"fit.Speci",
"fit.Preci",
"fit.FPR",
"fit.FNR",
"fit.FDR",
"fit.NPV",
"fit.FOR"),
legend.title = "Model",
spacing=1,
dot.size=1,
# colors = viridis::viridis(14)
colors = custom_palette
) + line0h +
theme(
text = element_text(size = 24), # Increase base font size
axis.title = element_text(size = 24), # Axis titles
axis.text = element_text(size = 22), # Axis text
legend.title = element_text(size = 24), # Legend title
legend.text = element_text(size = 22) # Legend text
) + theme_minimal()
gg88
Factor Reference Levels (reorder for comparison plots)
Code
df2 <- df0 %>%
mutate(
Ses = factor(Ses, levels=c("Morning", "Evening")),
Run = factor(Run, levels=c("01", "02", "03")),
Chrono = factor(Chrono, levels = c("Morning", "Evening")),
Condit = factor(Condit, levels = c("Congruent", "Incongruent")),
) %>%
mutate(
NumBack = as.integer(NumBack),
NumBack = paste0("n", sprintf("%02d", NumBack)),
NumBack = factor(NumBack, levels = c("n01", "n02", "n03")),
) %>%
identity()
df0 %>% dim()[1] 900 74[1] 900 74Code
REML <- TRUE
control <- lme4::lmerControl(optimizer = "Nelder_Mead")
fit.RT = lmerTest::lmer(formula=RT ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.TP = lmerTest::lmer(formula=TP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.FP = lmerTest::lmer(formula=FP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.TN = lmerTest::lmer(formula=TN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.FN = lmerTest::lmer(formula=FN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.Accur = lmerTest::lmer(formula=Accur ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.Sensi = lmerTest::lmer(formula=Sensi ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.Speci = lmerTest::lmer(formula=Speci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.Preci = lmerTest::lmer(formula=Preci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.FPR = lmerTest::lmer(formula=FPR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.FNR = lmerTest::lmer(formula=FNR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.FDR = lmerTest::lmer(formula=FDR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.NPV = lmerTest::lmer(formula=NPV ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)
fit.FOR = lmerTest::lmer(formula=FOR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 | Sub), data=df2, REML=REML, control=control)Settings
Check Models
Check Model fit.RT
fit.RT: [df2] RT ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: RT ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)
Data: df2
Control: control
REML criterion at convergence: -598.5
Scaled residuals:
Min 1Q Median 3Q Max
-2.9882 -0.6340 -0.0920 0.5384 4.4536
Random effects:
Groups Name Variance Std.Dev.
Sub (Intercept) 0.01608 0.1268
Residual 0.02447 0.1564
Number of obs: 898, groups: Sub, 51
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.563010 0.039196 129.924688 14.364 <2e-16 ***
SesEvening -0.018516 0.010535 844.251873 -1.758 0.0792 .
ChronoEvening 0.094996 0.037725 48.849313 2.518 0.0151 *
Run02 -0.015294 0.012783 839.480642 -1.196 0.2319
Run03 -0.011722 0.012793 839.465851 -0.916 0.3598
ConditIncongruent 0.024732 0.017516 887.868676 1.412 0.1583
NumBackn02 0.145712 0.012772 839.465851 11.409 <2e-16 ***
NumBackn03 0.276796 0.012796 839.524887 21.632 <2e-16 ***
KSS -0.006249 0.007382 881.152930 -0.847 0.3975
Genderm -0.032180 0.037596 47.425155 -0.856 0.3963
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---------------------------------------------------------------------
fit.RT: [df2] RT ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)
# R2 for Mixed Models
Conditional R2: 0.561
Marginal R2: 0.272
---------------------------------------------------------------------
fit.RT: [df2] RT ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)
# Intraclass Correlation Coefficient
Adjusted ICC: 0.397
Unadjusted ICC: 0.289
---------------------------------------------------------------------
fit.RT: [df2] RT ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)
# ICC by Group
Group | ICC
-------------
Sub | 0.397
--------------------------------------------------------------------- Effect of Ses
Code
fit.RT: [df2] RT ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)===================================================================== # Average predicted values of RT
Ses | Predicted | 95% CI
--------------------------------
Morning | 0.71 | 0.67, 0.75
Evening | 0.69 | 0.65, 0.73===================================================================== Ses | Predicted | 95% CI | p
-----------------------------------------
Evening | 0.69 | 0.65, 0.73 | < .001
Morning | 0.71 | 0.67, 0.75 | < .001===================================================================== # Pairwise comparisons
Ses | Contrast | 95% CI | p
------------------------------------------------
Evening-Morning | -0.02 | -0.04, 0.00 | 0.079Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Chrono
Code
fit.RT: [df2] RT ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)===================================================================== # Average predicted values of RT
Chrono | Predicted | 95% CI
--------------------------------
Morning | 0.65 | 0.60, 0.70
Evening | 0.75 | 0.70, 0.80===================================================================== Chrono | Predicted | 95% CI | p
-----------------------------------------
Evening | 0.75 | 0.70, 0.80 | < .001
Morning | 0.65 | 0.60, 0.70 | < .001===================================================================== # Pairwise comparisons
Chrono | Contrast | 95% CI | p
-----------------------------------------------
Evening-Morning | 0.09 | 0.02, 0.17 | 0.012Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Run
Code
fit.RT: [df2] RT ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)===================================================================== # Average predicted values of RT
Run | Predicted | 95% CI
----------------------------
01 | 0.71 | 0.67, 0.75
02 | 0.69 | 0.66, 0.73
03 | 0.70 | 0.66, 0.74===================================================================== Run | Predicted | 95% CI | p
-------------------------------------
01 | 0.71 | 0.67, 0.75 | < .001
02 | 0.69 | 0.66, 0.73 | < .001
03 | 0.70 | 0.66, 0.74 | < .001===================================================================== # Pairwise comparisons
Run | Contrast | 95% CI | p
---------------------------------------
01-02 | 0.02 | -0.01, 0.04 | 0.540
01-03 | 0.01 | -0.01, 0.04 | 0.540
02-03 | -3.57e-03 | -0.03, 0.02 | 0.780Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Condit
Code
fit.RT: [df2] RT ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)===================================================================== # Average predicted values of RT
Condit | Predicted | 95% CI
------------------------------------
Congruent | 0.69 | 0.65, 0.73
Incongruent | 0.71 | 0.67, 0.75===================================================================== Condit | Predicted | 95% CI | p
---------------------------------------------
Congruent | 0.69 | 0.65, 0.73 | < .001
Incongruent | 0.71 | 0.67, 0.75 | < .001===================================================================== # Pairwise comparisons
Condit | Contrast | 95% CI | p
------------------------------------------------------
Congruent-Incongruent | -0.02 | -0.06, 0.01 | 0.158Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of NumBack
Code
fit.RT: [df2] RT ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)===================================================================== # Average predicted values of RT
NumBack | Predicted | 95% CI
--------------------------------
n01 | 0.56 | 0.52, 0.60
n02 | 0.71 | 0.67, 0.75
n03 | 0.84 | 0.80, 0.88===================================================================== NumBack | Predicted | 95% CI | p
-----------------------------------------
n01 | 0.56 | 0.52, 0.60 | < .001
n02 | 0.71 | 0.67, 0.75 | < .001
n03 | 0.84 | 0.80, 0.88 | < .001===================================================================== # Pairwise comparisons
NumBack | Contrast | 95% CI | p
------------------------------------------
n01-n02 | -0.15 | -0.17, -0.12 | < .001
n01-n03 | -0.28 | -0.30, -0.25 | < .001
n02-n03 | -0.13 | -0.16, -0.11 | < .001Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of KSS
Code
fit.RT: [df2] RT ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)===================================================================== # Average predicted values of RT
KSS | Predicted | 95% CI
----------------------------
1 | 0.72 | 0.66, 0.78
2 | 0.72 | 0.67, 0.76
3 | 0.71 | 0.67, 0.75
4 | 0.70 | 0.67, 0.74
5 | 0.70 | 0.66, 0.73
6 | 0.69 | 0.65, 0.73
7 | 0.68 | 0.63, 0.74
8 | 0.68 | 0.61, 0.74===================================================================== # (Average) Linear trend for KSS
Slope | 95% CI | p
-------------------------------
-6.25e-03 | -0.02, 0.01 | 0.398===================================================================== # (Average) Linear trend for KSS
Slope | 95% CI | p
-------------------------------
-6.25e-03 | -0.02, 0.01 | 0.398Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Gender
Code
fit.RT: [df2] RT ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)===================================================================== # Average predicted values of RT
Gender | Predicted | 95% CI
-------------------------------
f | 0.72 | 0.67, 0.76
m | 0.68 | 0.63, 0.74===================================================================== Gender | Predicted | 95% CI | p
----------------------------------------
f | 0.72 | 0.67, 0.76 | < .001
m | 0.68 | 0.63, 0.74 | < .001===================================================================== # Pairwise comparisons
Gender | Contrast | 95% CI | p
---------------------------------------
f-m | 0.03 | -0.04, 0.11 | 0.392Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Check Model fit.TP
fit.TP: [df2] TP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: TP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)
Data: df2
Control: control
REML criterion at convergence: 2629.6
Scaled residuals:
Min 1Q Median 3Q Max
-3.7373 -0.4808 0.0824 0.5669 2.9202
Random effects:
Groups Name Variance Std.Dev.
Sub (Intercept) 0.3426 0.5853
Residual 0.9486 0.9740
Number of obs: 900, groups: Sub, 51
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 5.91639 0.21098 184.20095 28.042 < 2e-16 ***
SesEvening 0.12149 0.06545 849.19981 1.856 0.063771 .
ChronoEvening 0.07988 0.18071 50.24151 0.442 0.660365
Run02 0.20333 0.07952 842.18071 2.557 0.010736 *
Run03 0.19333 0.07952 842.18071 2.431 0.015260 *
ConditIncongruent 0.26092 0.10700 877.30135 2.439 0.014942 *
NumBackn02 -0.60000 0.07952 842.18071 -7.545 1.17e-13 ***
NumBackn03 -1.66333 0.07952 842.18071 -20.916 < 2e-16 ***
KSS -0.15927 0.04479 821.61689 -3.556 0.000398 ***
Genderm 0.38904 0.17924 48.07800 2.170 0.034941 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---------------------------------------------------------------------
fit.TP: [df2] TP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)
# R2 for Mixed Models
Conditional R2: 0.485
Marginal R2: 0.299
---------------------------------------------------------------------
fit.TP: [df2] TP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)
# Intraclass Correlation Coefficient
Adjusted ICC: 0.265
Unadjusted ICC: 0.186
---------------------------------------------------------------------
fit.TP: [df2] TP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)
# ICC by Group
Group | ICC
-------------
Sub | 0.265
--------------------------------------------------------------------- Effect of Ses
Code
fit.TP: [df2] TP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)===================================================================== # Average predicted values of TP
Ses | Predicted | 95% CI
--------------------------------
Morning | 4.97 | 4.78, 5.15
Evening | 5.09 | 4.90, 5.27===================================================================== Ses | Predicted | 95% CI | p
-----------------------------------------
Evening | 5.09 | 4.90, 5.27 | < .001
Morning | 4.97 | 4.78, 5.15 | < .001===================================================================== # Pairwise comparisons
Ses | Contrast | 95% CI | p
------------------------------------------------
Evening-Morning | 0.12 | -0.01, 0.25 | 0.064Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Chrono
Code
fit.TP: [df2] TP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)===================================================================== # Average predicted values of TP
Chrono | Predicted | 95% CI
--------------------------------
Morning | 4.99 | 4.74, 5.24
Evening | 5.07 | 4.82, 5.32===================================================================== Chrono | Predicted | 95% CI | p
-----------------------------------------
Evening | 5.07 | 4.82, 5.32 | < .001
Morning | 4.99 | 4.74, 5.24 | < .001===================================================================== # Pairwise comparisons
Chrono | Contrast | 95% CI | p
------------------------------------------------
Evening-Morning | 0.08 | -0.27, 0.43 | 0.659Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Run
Code
fit.TP: [df2] TP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)===================================================================== # Average predicted values of TP
Run | Predicted | 95% CI
----------------------------
01 | 4.90 | 4.70, 5.09
02 | 5.10 | 4.90, 5.30
03 | 5.09 | 4.89, 5.29===================================================================== Run | Predicted | 95% CI | p
-------------------------------------
01 | 4.90 | 4.70, 5.09 | < .001
02 | 5.10 | 4.90, 5.30 | < .001
03 | 5.09 | 4.89, 5.29 | < .001===================================================================== # Pairwise comparisons
Run | Contrast | 95% CI | p
---------------------------------------
01-02 | -0.20 | -0.36, -0.05 | 0.023
01-03 | -0.19 | -0.35, -0.04 | 0.023
02-03 | 0.01 | -0.15, 0.17 | 0.900Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Condit
Code
fit.TP: [df2] TP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)===================================================================== # Average predicted values of TP
Condit | Predicted | 95% CI
------------------------------------
Congruent | 4.90 | 4.70, 5.10
Incongruent | 5.16 | 4.96, 5.36===================================================================== Condit | Predicted | 95% CI | p
---------------------------------------------
Congruent | 4.90 | 4.70, 5.10 | < .001
Incongruent | 5.16 | 4.96, 5.36 | < .001===================================================================== # Pairwise comparisons
Condit | Contrast | 95% CI | p
-------------------------------------------------------
Congruent-Incongruent | -0.26 | -0.47, -0.05 | 0.015Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of NumBack
Code
fit.TP: [df2] TP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)===================================================================== # Average predicted values of TP
NumBack | Predicted | 95% CI
--------------------------------
n01 | 5.78 | 5.59, 5.98
n02 | 5.18 | 4.99, 5.38
n03 | 4.12 | 3.92, 4.32===================================================================== NumBack | Predicted | 95% CI | p
-----------------------------------------
n01 | 5.78 | 5.59, 5.98 | < .001
n02 | 5.18 | 4.99, 5.38 | < .001
n03 | 4.12 | 3.92, 4.32 | < .001===================================================================== # Pairwise comparisons
NumBack | Contrast | 95% CI | p
----------------------------------------
n01-n02 | 0.60 | 0.44, 0.76 | < .001
n01-n03 | 1.66 | 1.51, 1.82 | < .001
n02-n03 | 1.06 | 0.91, 1.22 | < .001Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of KSS
Code
fit.TP: [df2] TP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)===================================================================== # Average predicted values of TP
KSS | Predicted | 95% CI
----------------------------
1 | 5.54 | 5.21, 5.87
2 | 5.38 | 5.12, 5.64
3 | 5.22 | 5.02, 5.43
4 | 5.07 | 4.89, 5.24
5 | 4.91 | 4.72, 5.09
6 | 4.75 | 4.51, 4.98
7 | 4.59 | 4.29, 4.89
8 | 4.43 | 4.05, 4.80===================================================================== # (Average) Linear trend for KSS
Slope | 95% CI | p
-----------------------------
-0.16 | -0.25, -0.07 | < .001===================================================================== # (Average) Linear trend for KSS
Slope | 95% CI | p
-----------------------------
-0.16 | -0.25, -0.07 | < .001Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Gender
Code
fit.TP: [df2] TP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)===================================================================== # Average predicted values of TP
Gender | Predicted | 95% CI
-------------------------------
f | 4.85 | 4.62, 5.09
m | 5.24 | 4.98, 5.50===================================================================== Gender | Predicted | 95% CI | p
----------------------------------------
f | 4.85 | 4.62, 5.09 | < .001
m | 5.24 | 4.98, 5.50 | < .001===================================================================== # Pairwise comparisons
Gender | Contrast | 95% CI | p
----------------------------------------
f-m | -0.39 | -0.74, -0.04 | 0.030Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Check Model fit.FP
fit.FP: [df2] FP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: FP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)
Data: df2
Control: control
REML criterion at convergence: 2669.4
Scaled residuals:
Min 1Q Median 3Q Max
-2.6401 -0.5830 -0.0873 0.3781 11.1668
Random effects:
Groups Name Variance Std.Dev.
Sub (Intercept) 0.3082 0.5551
Residual 0.9992 0.9996
Number of obs: 900, groups: Sub, 51
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.004144 0.209585 199.696091 0.020 0.9842
SesEvening 0.085549 0.067151 849.987515 1.274 0.2030
ChronoEvening 0.037219 0.173671 50.526846 0.214 0.8312
Run02 -0.100000 0.081618 842.274735 -1.225 0.2208
Run03 -0.153333 0.081618 842.274735 -1.879 0.0606 .
ConditIncongruent -0.008700 0.109183 867.454730 -0.080 0.9365
NumBackn02 0.706667 0.081618 842.274735 8.658 <2e-16 ***
NumBackn03 1.330000 0.081618 842.274735 16.295 <2e-16 ***
KSS 0.076137 0.045579 791.490956 1.670 0.0952 .
Genderm -0.213472 0.171999 48.142738 -1.241 0.2206
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---------------------------------------------------------------------
fit.FP: [df2] FP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)
# R2 for Mixed Models
Conditional R2: 0.387
Marginal R2: 0.198
---------------------------------------------------------------------
fit.FP: [df2] FP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)
# Intraclass Correlation Coefficient
Adjusted ICC: 0.236
Unadjusted ICC: 0.189
---------------------------------------------------------------------
fit.FP: [df2] FP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)
# ICC by Group
Group | ICC
-------------
Sub | 0.236
--------------------------------------------------------------------- Effect of Ses
Code
fit.FP: [df2] FP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)===================================================================== # Average predicted values of FP
Ses | Predicted | 95% CI
--------------------------------
Morning | 0.84 | 0.66, 1.01
Evening | 0.92 | 0.74, 1.10===================================================================== Ses | Predicted | 95% CI | p
-----------------------------------------
Evening | 0.92 | 0.74, 1.10 | < .001
Morning | 0.84 | 0.66, 1.01 | < .001===================================================================== # Pairwise comparisons
Ses | Contrast | 95% CI | p
------------------------------------------------
Evening-Morning | 0.09 | -0.05, 0.22 | 0.203Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Chrono
Code
fit.FP: [df2] FP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)===================================================================== # Average predicted values of FP
Chrono | Predicted | 95% CI
--------------------------------
Morning | 0.86 | 0.62, 1.10
Evening | 0.90 | 0.66, 1.14===================================================================== Chrono | Predicted | 95% CI | p
-----------------------------------------
Evening | 0.90 | 0.66, 1.14 | < .001
Morning | 0.86 | 0.62, 1.10 | < .001===================================================================== # Pairwise comparisons
Chrono | Contrast | 95% CI | p
------------------------------------------------
Evening-Morning | 0.04 | -0.30, 0.38 | 0.830Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Run
Code
fit.FP: [df2] FP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)===================================================================== # Average predicted values of FP
Run | Predicted | 95% CI
----------------------------
01 | 0.96 | 0.77, 1.15
02 | 0.86 | 0.67, 1.05
03 | 0.81 | 0.62, 1.00===================================================================== Run | Predicted | 95% CI | p
-------------------------------------
01 | 0.96 | 0.77, 1.15 | < .001
02 | 0.86 | 0.67, 1.05 | < .001
03 | 0.81 | 0.62, 1.00 | < .001===================================================================== # Pairwise comparisons
Run | Contrast | 95% CI | p
--------------------------------------
01-02 | 0.10 | -0.06, 0.26 | 0.331
01-03 | 0.15 | -0.01, 0.31 | 0.182
02-03 | 0.05 | -0.11, 0.21 | 0.514Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Condit
Code
fit.FP: [df2] FP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)===================================================================== # Average predicted values of FP
Condit | Predicted | 95% CI
------------------------------------
Congruent | 0.88 | 0.69, 1.08
Incongruent | 0.87 | 0.68, 1.07===================================================================== Condit | Predicted | 95% CI | p
---------------------------------------------
Congruent | 0.88 | 0.69, 1.08 | < .001
Incongruent | 0.87 | 0.68, 1.07 | < .001===================================================================== # Pairwise comparisons
Condit | Contrast | 95% CI | p
------------------------------------------------------
Congruent-Incongruent | 8.70e-03 | -0.21, 0.22 | 0.937Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of NumBack
Code
fit.FP: [df2] FP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)===================================================================== # Average predicted values of FP
NumBack | Predicted | 95% CI
--------------------------------
n01 | 0.20 | 0.01, 0.39
n02 | 0.91 | 0.72, 1.10
n03 | 1.53 | 1.34, 1.72===================================================================== NumBack | Predicted | 95% CI | p
-----------------------------------------
n01 | 0.20 | 0.01, 0.39 | 0.039
n02 | 0.91 | 0.72, 1.10 | < .001
n03 | 1.53 | 1.34, 1.72 | < .001===================================================================== # Pairwise comparisons
NumBack | Contrast | 95% CI | p
------------------------------------------
n01-n02 | -0.71 | -0.87, -0.55 | < .001
n01-n03 | -1.33 | -1.49, -1.17 | < .001
n02-n03 | -0.62 | -0.78, -0.46 | < .001Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of KSS
Code
fit.FP: [df2] FP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)===================================================================== # Average predicted values of FP
KSS | Predicted | 95% CI
----------------------------
1 | 0.63 | 0.30, 0.97
2 | 0.71 | 0.45, 0.97
3 | 0.79 | 0.59, 0.98
4 | 0.86 | 0.69, 1.03
5 | 0.94 | 0.76, 1.12
6 | 1.01 | 0.78, 1.24
7 | 1.09 | 0.79, 1.39
8 | 1.17 | 0.79, 1.54===================================================================== # (Average) Linear trend for KSS
Slope | 95% CI | p
---------------------------
0.08 | -0.01, 0.17 | 0.095===================================================================== # (Average) Linear trend for KSS
Slope | 95% CI | p
---------------------------
0.08 | -0.01, 0.17 | 0.095Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Gender
Code
fit.FP: [df2] FP ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)===================================================================== # Average predicted values of FP
Gender | Predicted | 95% CI
-------------------------------
f | 0.97 | 0.75, 1.20
m | 0.76 | 0.51, 1.01===================================================================== Gender | Predicted | 95% CI | p
----------------------------------------
f | 0.97 | 0.75, 1.20 | < .001
m | 0.76 | 0.51, 1.01 | < .001===================================================================== # Pairwise comparisons
Gender | Contrast | 95% CI | p
---------------------------------------
f-m | 0.21 | -0.12, 0.55 | 0.215Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Check Model fit.TN
fit.TN: [df2] TN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: TN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)
Data: df2
Control: control
REML criterion at convergence: 3535.6
Scaled residuals:
Min 1Q Median 3Q Max
-7.0504 -0.3163 0.0507 0.4047 5.3593
Random effects:
Groups Name Variance Std.Dev.
Sub (Intercept) 1.648 1.284
Residual 2.553 1.598
Number of obs: 900, groups: Sub, 51
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 18.24224 0.39841 131.96677 45.788 < 2e-16 ***
SesEvening 0.29218 0.10747 846.46183 2.719 0.00669 **
ChronoEvening 0.51673 0.38220 49.13531 1.352 0.18257
Run02 0.10000 0.13046 841.73880 0.766 0.44360
Run03 0.15333 0.13046 841.73880 1.175 0.24021
ConditIncongruent 1.05224 0.17855 889.94626 5.893 5.36e-09 ***
NumBackn02 -0.70667 0.13046 841.73880 -5.417 7.93e-08 ***
NumBackn03 -1.33000 0.13046 841.73880 -10.194 < 2e-16 ***
KSS -0.43029 0.07532 882.23254 -5.713 1.52e-08 ***
Genderm 0.39981 0.38088 47.70375 1.050 0.29914
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---------------------------------------------------------------------
fit.TN: [df2] TN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)
# R2 for Mixed Models
Conditional R2: 0.466
Marginal R2: 0.122
---------------------------------------------------------------------
fit.TN: [df2] TN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)
# Intraclass Correlation Coefficient
Adjusted ICC: 0.392
Unadjusted ICC: 0.344
---------------------------------------------------------------------
fit.TN: [df2] TN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)
# ICC by Group
Group | ICC
-------------
Sub | 0.392
--------------------------------------------------------------------- Effect of Ses
Code
fit.TN: [df2] TN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)===================================================================== # Average predicted values of TN
Ses | Predicted | 95% CI
----------------------------------
Morning | 16.80 | 16.41, 17.18
Evening | 17.09 | 16.70, 17.47===================================================================== Ses | Predicted | 95% CI | p
-------------------------------------------
Evening | 17.09 | 16.70, 17.47 | < .001
Morning | 16.80 | 16.41, 17.18 | < .001===================================================================== # Pairwise comparisons
Ses | Contrast | 95% CI | p
-----------------------------------------------
Evening-Morning | 0.29 | 0.08, 0.50 | 0.007Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Chrono
Code
fit.TN: [df2] TN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)===================================================================== # Average predicted values of TN
Chrono | Predicted | 95% CI
----------------------------------
Morning | 16.68 | 16.16, 17.20
Evening | 17.20 | 16.67, 17.73===================================================================== Chrono | Predicted | 95% CI | p
-------------------------------------------
Evening | 17.20 | 16.67, 17.73 | < .001
Morning | 16.68 | 16.16, 17.20 | < .001===================================================================== # Pairwise comparisons
Chrono | Contrast | 95% CI | p
------------------------------------------------
Evening-Morning | 0.52 | -0.23, 1.27 | 0.177Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Run
Code
fit.TN: [df2] TN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)===================================================================== # Average predicted values of TN
Run | Predicted | 95% CI
------------------------------
01 | 16.86 | 16.46, 17.25
02 | 16.96 | 16.56, 17.35
03 | 17.01 | 16.61, 17.41===================================================================== Run | Predicted | 95% CI | p
---------------------------------------
01 | 16.86 | 16.46, 17.25 | < .001
02 | 16.96 | 16.56, 17.35 | < .001
03 | 17.01 | 16.61, 17.41 | < .001===================================================================== # Pairwise comparisons
Run | Contrast | 95% CI | p
--------------------------------------
01-02 | -0.10 | -0.36, 0.16 | 0.665
01-03 | -0.15 | -0.41, 0.10 | 0.665
02-03 | -0.05 | -0.31, 0.20 | 0.683Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Condit
Code
fit.TN: [df2] TN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)===================================================================== # Average predicted values of TN
Condit | Predicted | 95% CI
--------------------------------------
Congruent | 16.41 | 16.01, 16.82
Incongruent | 17.47 | 17.06, 17.87===================================================================== Condit | Predicted | 95% CI | p
-----------------------------------------------
Congruent | 16.41 | 16.01, 16.82 | < .001
Incongruent | 17.47 | 17.06, 17.87 | < .001===================================================================== # Pairwise comparisons
Condit | Contrast | 95% CI | p
--------------------------------------------------------
Congruent-Incongruent | -1.05 | -1.40, -0.70 | < .001Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of NumBack
Code
fit.TN: [df2] TN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)===================================================================== # Average predicted values of TN
NumBack | Predicted | 95% CI
----------------------------------
n01 | 17.62 | 17.22, 18.02
n02 | 16.91 | 16.52, 17.31
n03 | 16.29 | 15.89, 16.69===================================================================== NumBack | Predicted | 95% CI | p
-------------------------------------------
n01 | 17.62 | 17.22, 18.02 | < .001
n02 | 16.91 | 16.52, 17.31 | < .001
n03 | 16.29 | 15.89, 16.69 | < .001===================================================================== # Pairwise comparisons
NumBack | Contrast | 95% CI | p
----------------------------------------
n01-n02 | 0.71 | 0.45, 0.96 | < .001
n01-n03 | 1.33 | 1.07, 1.59 | < .001
n02-n03 | 0.62 | 0.37, 0.88 | < .001Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of KSS
Code
fit.TN: [df2] TN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)===================================================================== # Average predicted values of TN
KSS | Predicted | 95% CI
------------------------------
1 | 18.33 | 17.73, 18.93
2 | 17.90 | 17.41, 18.39
3 | 17.47 | 17.06, 17.88
4 | 17.04 | 16.67, 17.41
5 | 16.61 | 16.22, 17.00
6 | 16.18 | 15.73, 16.63
7 | 15.75 | 15.20, 16.30
8 | 15.32 | 14.65, 15.99===================================================================== # (Average) Linear trend for KSS
Slope | 95% CI | p
-----------------------------
-0.43 | -0.58, -0.28 | < .001===================================================================== # (Average) Linear trend for KSS
Slope | 95% CI | p
-----------------------------
-0.43 | -0.58, -0.28 | < .001Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Gender
Code
fit.TN: [df2] TN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)===================================================================== # Average predicted values of TN
Gender | Predicted | 95% CI
---------------------------------
f | 16.76 | 16.26, 17.26
m | 17.16 | 16.61, 17.71===================================================================== Gender | Predicted | 95% CI | p
------------------------------------------
f | 16.76 | 16.26, 17.26 | < .001
m | 17.16 | 16.61, 17.71 | < .001===================================================================== # Pairwise comparisons
Gender | Contrast | 95% CI | p
---------------------------------------
f-m | -0.40 | -1.15, 0.35 | 0.294Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Check Model fit.FN
fit.FN: [df2] FN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: FN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)
Data: df2
Control: control
REML criterion at convergence: 2475.8
Scaled residuals:
Min 1Q Median 3Q Max
-2.5049 -0.6100 -0.0826 0.4990 3.6960
Random effects:
Groups Name Variance Std.Dev.
Sub (Intercept) 0.2539 0.5039
Residual 0.8030 0.8961
Number of obs: 900, groups: Sub, 51
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.139069 0.188825 198.277405 0.736 0.46230
SesEvening 0.003268 0.060201 850.117631 0.054 0.95672
ChronoEvening 0.098384 0.157291 50.799611 0.625 0.53445
Run02 -0.203333 0.073167 842.560847 -2.779 0.00557 **
Run03 -0.193333 0.073167 842.560847 -2.642 0.00839 **
ConditIncongruent 0.068652 0.097970 869.353647 0.701 0.48365
NumBackn02 0.600000 0.073167 842.560847 8.200 8.9e-16 ***
NumBackn03 1.663333 0.073167 842.560847 22.733 < 2e-16 ***
KSS 0.050328 0.040917 797.187750 1.230 0.21906
Genderm -0.324170 0.155816 48.436707 -2.080 0.04280 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---------------------------------------------------------------------
fit.FN: [df2] FN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)
# R2 for Mixed Models
Conditional R2: 0.490
Marginal R2: 0.329
---------------------------------------------------------------------
fit.FN: [df2] FN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)
# Intraclass Correlation Coefficient
Adjusted ICC: 0.240
Unadjusted ICC: 0.161
---------------------------------------------------------------------
fit.FN: [df2] FN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)
# ICC by Group
Group | ICC
-------------
Sub | 0.240
--------------------------------------------------------------------- Effect of Ses
Code
fit.FN: [df2] FN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)===================================================================== # Average predicted values of FN
Ses | Predicted | 95% CI
--------------------------------
Morning | 0.91 | 0.75, 1.07
Evening | 0.91 | 0.75, 1.07===================================================================== Ses | Predicted | 95% CI | p
-----------------------------------------
Evening | 0.91 | 0.75, 1.07 | < .001
Morning | 0.91 | 0.75, 1.07 | < .001===================================================================== # Pairwise comparisons
Ses | Contrast | 95% CI | p
------------------------------------------------
Evening-Morning | 3.27e-03 | -0.11, 0.12 | 0.957Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Chrono
Code
fit.FN: [df2] FN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)===================================================================== # Average predicted values of FN
Chrono | Predicted | 95% CI
--------------------------------
Morning | 0.86 | 0.65, 1.08
Evening | 0.96 | 0.74, 1.18===================================================================== Chrono | Predicted | 95% CI | p
-----------------------------------------
Evening | 0.96 | 0.74, 1.18 | < .001
Morning | 0.86 | 0.65, 1.08 | < .001===================================================================== # Pairwise comparisons
Chrono | Contrast | 95% CI | p
------------------------------------------------
Evening-Morning | 0.10 | -0.21, 0.41 | 0.532Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Run
Code
fit.FN: [df2] FN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)===================================================================== # Average predicted values of FN
Run | Predicted | 95% CI
----------------------------
01 | 1.04 | 0.87, 1.22
02 | 0.84 | 0.67, 1.01
03 | 0.85 | 0.68, 1.02===================================================================== Run | Predicted | 95% CI | p
-------------------------------------
01 | 1.04 | 0.87, 1.22 | < .001
02 | 0.84 | 0.67, 1.01 | < .001
03 | 0.85 | 0.68, 1.02 | < .001===================================================================== # Pairwise comparisons
Run | Contrast | 95% CI | p
--------------------------------------
01-02 | 0.20 | 0.06, 0.35 | 0.013
01-03 | 0.19 | 0.05, 0.34 | 0.013
02-03 | -0.01 | -0.15, 0.13 | 0.891Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Condit
Code
fit.FN: [df2] FN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)===================================================================== # Average predicted values of FN
Condit | Predicted | 95% CI
------------------------------------
Congruent | 0.88 | 0.70, 1.06
Incongruent | 0.95 | 0.77, 1.12===================================================================== Condit | Predicted | 95% CI | p
---------------------------------------------
Congruent | 0.88 | 0.70, 1.06 | < .001
Incongruent | 0.95 | 0.77, 1.12 | < .001===================================================================== # Pairwise comparisons
Condit | Contrast | 95% CI | p
------------------------------------------------------
Congruent-Incongruent | -0.07 | -0.26, 0.12 | 0.484Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of NumBack
Code
fit.FN: [df2] FN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)===================================================================== # Average predicted values of FN
NumBack | Predicted | 95% CI
---------------------------------
n01 | 0.16 | -0.02, 0.33
n02 | 0.76 | 0.58, 0.93
n03 | 1.82 | 1.65, 1.99===================================================================== NumBack | Predicted | 95% CI | p
------------------------------------------
n01 | 0.16 | -0.02, 0.33 | 0.074
n02 | 0.76 | 0.58, 0.93 | < .001
n03 | 1.82 | 1.65, 1.99 | < .001===================================================================== # Pairwise comparisons
NumBack | Contrast | 95% CI | p
------------------------------------------
n01-n02 | -0.60 | -0.74, -0.46 | < .001
n01-n03 | -1.66 | -1.81, -1.52 | < .001
n02-n03 | -1.06 | -1.21, -0.92 | < .001Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of KSS
Code
fit.FN: [df2] FN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)===================================================================== # Average predicted values of FN
KSS | Predicted | 95% CI
----------------------------
1 | 0.75 | 0.45, 1.05
2 | 0.80 | 0.57, 1.03
3 | 0.85 | 0.67, 1.03
4 | 0.90 | 0.75, 1.05
5 | 0.95 | 0.79, 1.11
6 | 1.00 | 0.79, 1.21
7 | 1.05 | 0.78, 1.32
8 | 1.10 | 0.76, 1.44===================================================================== # (Average) Linear trend for KSS
Slope | 95% CI | p
---------------------------
0.05 | -0.03, 0.13 | 0.219===================================================================== # (Average) Linear trend for KSS
Slope | 95% CI | p
---------------------------
0.05 | -0.03, 0.13 | 0.219Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Gender
Code
fit.FN: [df2] FN ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender + (1 |
Sub)===================================================================== # Average predicted values of FN
Gender | Predicted | 95% CI
-------------------------------
f | 1.06 | 0.85, 1.26
m | 0.73 | 0.51, 0.96===================================================================== Gender | Predicted | 95% CI | p
----------------------------------------
f | 1.06 | 0.85, 1.26 | < .001
m | 0.73 | 0.51, 0.96 | < .001===================================================================== # Pairwise comparisons
Gender | Contrast | 95% CI | p
--------------------------------------
f-m | 0.32 | 0.02, 0.63 | 0.038Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Check Model fit.Accur
fit.Accur: [df2] Accur ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Accur ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)
Data: df2
Control: control
REML criterion at convergence: -2238.7
Scaled residuals:
Min 1Q Median 3Q Max
-7.4909 -0.4963 0.1001 0.6073 2.4745
Random effects:
Groups Name Variance Std.Dev.
Sub (Intercept) 0.001416 0.03762
Residual 0.003888 0.06236
Number of obs: 891, groups: Sub, 51
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 9.952e-01 1.354e-02 1.830e+02 73.501 < 2e-16 ***
SesEvening -2.813e-03 4.232e-03 8.450e+02 -0.665 0.50647
ChronoEvening -4.329e-03 1.164e-02 5.054e+01 -0.372 0.71154
Run02 1.277e-02 5.117e-03 8.333e+02 2.495 0.01279 *
Run03 1.459e-02 5.117e-03 8.333e+02 2.851 0.00446 **
ConditIncongruent 1.720e-06 7.031e-03 8.583e+02 0.000 0.99980
NumBackn02 -5.499e-02 5.117e-03 8.333e+02 -10.747 < 2e-16 ***
NumBackn03 -1.260e-01 5.117e-03 8.333e+02 -24.620 < 2e-16 ***
KSS -6.127e-03 2.916e-03 8.003e+02 -2.101 0.03593 *
Genderm 2.280e-02 1.152e-02 4.805e+01 1.980 0.05350 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---------------------------------------------------------------------
fit.Accur: [df2] Accur ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)
# R2 for Mixed Models
Conditional R2: 0.527
Marginal R2: 0.355
---------------------------------------------------------------------
fit.Accur: [df2] Accur ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)
# Intraclass Correlation Coefficient
Adjusted ICC: 0.267
Unadjusted ICC: 0.172
---------------------------------------------------------------------
fit.Accur: [df2] Accur ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)
# ICC by Group
Group | ICC
-------------
Sub | 0.267
--------------------------------------------------------------------- Effect of Ses
Code
fit.Accur: [df2] Accur ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of Accur
Ses | Predicted | 95% CI
--------------------------------
Morning | 0.93 | 0.91, 0.94
Evening | 0.92 | 0.91, 0.94===================================================================== Ses | Predicted | 95% CI | p
-----------------------------------------
Evening | 0.92 | 0.91, 0.94 | < .001
Morning | 0.93 | 0.91, 0.94 | < .001===================================================================== # Pairwise comparisons
Ses | Contrast | 95% CI | p
-------------------------------------------------
Evening-Morning | -2.81e-03 | -0.01, 0.01 | 0.506Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Chrono
Code
fit.Accur: [df2] Accur ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of Accur
Chrono | Predicted | 95% CI
--------------------------------
Morning | 0.93 | 0.91, 0.94
Evening | 0.92 | 0.91, 0.94===================================================================== Chrono | Predicted | 95% CI | p
-----------------------------------------
Evening | 0.92 | 0.91, 0.94 | < .001
Morning | 0.93 | 0.91, 0.94 | < .001===================================================================== # Pairwise comparisons
Chrono | Contrast | 95% CI | p
-------------------------------------------------
Evening-Morning | -4.33e-03 | -0.03, 0.02 | 0.710Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Run
Code
fit.Accur: [df2] Accur ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of Accur
Run | Predicted | 95% CI
----------------------------
01 | 0.92 | 0.90, 0.93
02 | 0.93 | 0.92, 0.94
03 | 0.93 | 0.92, 0.94===================================================================== Run | Predicted | 95% CI | p
-------------------------------------
01 | 0.92 | 0.90, 0.93 | < .001
02 | 0.93 | 0.92, 0.94 | < .001
03 | 0.93 | 0.92, 0.94 | < .001===================================================================== # Pairwise comparisons
Run | Contrast | 95% CI | p
----------------------------------------
01-02 | -0.01 | -0.02, 0.00 | 0.019
01-03 | -0.01 | -0.02, 0.00 | 0.013
02-03 | -1.82e-03 | -0.01, 0.01 | 0.722Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Condit
Code
fit.Accur: [df2] Accur ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of Accur
Condit | Predicted | 95% CI
------------------------------------
Congruent | 0.92 | 0.91, 0.94
Incongruent | 0.92 | 0.91, 0.94===================================================================== Condit | Predicted | 95% CI | p
---------------------------------------------
Congruent | 0.92 | 0.91, 0.94 | < .001
Incongruent | 0.92 | 0.91, 0.94 | < .001===================================================================== # Pairwise comparisons
Condit | Contrast | 95% CI | p
--------------------------------------------------------
Congruent-Incongruent | -1.72e-06 | -0.01, 0.01 | > .999Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of NumBack
Code
fit.Accur: [df2] Accur ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of Accur
NumBack | Predicted | 95% CI
--------------------------------
n01 | 0.98 | 0.97, 1.00
n02 | 0.93 | 0.92, 0.94
n03 | 0.86 | 0.85, 0.87===================================================================== NumBack | Predicted | 95% CI | p
-----------------------------------------
n01 | 0.98 | 0.97, 1.00 | < .001
n02 | 0.93 | 0.92, 0.94 | < .001
n03 | 0.86 | 0.85, 0.87 | < .001===================================================================== # Pairwise comparisons
NumBack | Contrast | 95% CI | p
----------------------------------------
n01-n02 | 0.05 | 0.04, 0.07 | < .001
n01-n03 | 0.13 | 0.12, 0.14 | < .001
n02-n03 | 0.07 | 0.06, 0.08 | < .001Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of KSS
Code
fit.Accur: [df2] Accur ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of Accur
KSS | Predicted | 95% CI
----------------------------
1 | 0.94 | 0.92, 0.97
2 | 0.94 | 0.92, 0.96
3 | 0.93 | 0.92, 0.95
4 | 0.93 | 0.91, 0.94
5 | 0.92 | 0.91, 0.93
6 | 0.91 | 0.90, 0.93
7 | 0.91 | 0.89, 0.93
8 | 0.90 | 0.88, 0.93===================================================================== # (Average) Linear trend for KSS
Slope | 95% CI | p
--------------------------------
-6.13e-03 | -0.01, 0.00 | 0.036===================================================================== # (Average) Linear trend for KSS
Slope | 95% CI | p
--------------------------------
-6.13e-03 | -0.01, 0.00 | 0.036Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Gender
Code
fit.Accur: [df2] Accur ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of Accur
Gender | Predicted | 95% CI
-------------------------------
f | 0.91 | 0.90, 0.93
m | 0.94 | 0.92, 0.95===================================================================== Gender | Predicted | 95% CI | p
----------------------------------------
f | 0.91 | 0.90, 0.93 | < .001
m | 0.94 | 0.92, 0.95 | < .001===================================================================== # Pairwise comparisons
Gender | Contrast | 95% CI | p
----------------------------------------
f-m | -0.02 | -0.05, 0.00 | 0.048Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Check Model fit.Sensi
fit.Sensi: [df2] Sensi ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Sensi ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)
Data: df2
Control: control
REML criterion at convergence: -704.5
Scaled residuals:
Min 1Q Median 3Q Max
-3.6616 -0.4992 0.0791 0.6101 2.5049
Random effects:
Groups Name Variance Std.Dev.
Sub (Intercept) 0.006864 0.08285
Residual 0.022360 0.14953
Number of obs: 891, groups: Sub, 51
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.978820 0.031344 200.304268 31.228 < 2e-16 ***
SesEvening 0.001712 0.010144 846.426854 0.169 0.86597
ChronoEvening -0.013169 0.026008 51.086968 -0.506 0.61478
Run02 0.034231 0.012271 833.660412 2.790 0.00540 **
Run03 0.032548 0.012271 833.660412 2.652 0.00814 **
ConditIncongruent -0.005493 0.016743 843.615673 -0.328 0.74293
NumBackn02 -0.101010 0.012271 833.660412 -8.232 7.09e-16 ***
NumBackn03 -0.280022 0.012271 833.660412 -22.820 < 2e-16 ***
KSS -0.010352 0.006924 765.128571 -1.495 0.13533
Genderm 0.055245 0.025690 48.327741 2.150 0.03655 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---------------------------------------------------------------------
fit.Sensi: [df2] Sensi ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)
# R2 for Mixed Models
Conditional R2: 0.491
Marginal R2: 0.335
---------------------------------------------------------------------
fit.Sensi: [df2] Sensi ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)
# Intraclass Correlation Coefficient
Adjusted ICC: 0.235
Unadjusted ICC: 0.156
---------------------------------------------------------------------
fit.Sensi: [df2] Sensi ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)
# ICC by Group
Group | ICC
-------------
Sub | 0.235
--------------------------------------------------------------------- Effect of Ses
Code
fit.Sensi: [df2] Sensi ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of Sensi
Ses | Predicted | 95% CI
--------------------------------
Morning | 0.85 | 0.82, 0.87
Evening | 0.85 | 0.82, 0.87===================================================================== Ses | Predicted | 95% CI | p
-----------------------------------------
Evening | 0.85 | 0.82, 0.87 | < .001
Morning | 0.85 | 0.82, 0.87 | < .001===================================================================== # Pairwise comparisons
Ses | Contrast | 95% CI | p
------------------------------------------------
Evening-Morning | 1.71e-03 | -0.02, 0.02 | 0.866Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Chrono
Code
fit.Sensi: [df2] Sensi ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of Sensi
Chrono | Predicted | 95% CI
--------------------------------
Morning | 0.85 | 0.82, 0.89
Evening | 0.84 | 0.80, 0.88===================================================================== Chrono | Predicted | 95% CI | p
-----------------------------------------
Evening | 0.84 | 0.80, 0.88 | < .001
Morning | 0.85 | 0.82, 0.89 | < .001===================================================================== # Pairwise comparisons
Chrono | Contrast | 95% CI | p
------------------------------------------------
Evening-Morning | -0.01 | -0.06, 0.04 | 0.613Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Run
Code
fit.Sensi: [df2] Sensi ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of Sensi
Run | Predicted | 95% CI
----------------------------
01 | 0.82 | 0.80, 0.85
02 | 0.86 | 0.83, 0.89
03 | 0.86 | 0.83, 0.89===================================================================== Run | Predicted | 95% CI | p
-------------------------------------
01 | 0.82 | 0.80, 0.85 | < .001
02 | 0.86 | 0.83, 0.89 | < .001
03 | 0.86 | 0.83, 0.89 | < .001===================================================================== # Pairwise comparisons
Run | Contrast | 95% CI | p
---------------------------------------
01-02 | -0.03 | -0.06, -0.01 | 0.012
01-03 | -0.03 | -0.06, -0.01 | 0.012
02-03 | 1.68e-03 | -0.02, 0.03 | 0.891Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Condit
Code
fit.Sensi: [df2] Sensi ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of Sensi
Condit | Predicted | 95% CI
------------------------------------
Congruent | 0.85 | 0.82, 0.88
Incongruent | 0.84 | 0.81, 0.87===================================================================== Condit | Predicted | 95% CI | p
---------------------------------------------
Congruent | 0.85 | 0.82, 0.88 | < .001
Incongruent | 0.84 | 0.81, 0.87 | < .001===================================================================== # Pairwise comparisons
Condit | Contrast | 95% CI | p
------------------------------------------------------
Congruent-Incongruent | 5.49e-03 | -0.03, 0.04 | 0.743Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of NumBack
Code
fit.Sensi: [df2] Sensi ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of Sensi
NumBack | Predicted | 95% CI
--------------------------------
n01 | 0.97 | 0.95, 1.00
n02 | 0.87 | 0.84, 0.90
n03 | 0.69 | 0.67, 0.72===================================================================== NumBack | Predicted | 95% CI | p
-----------------------------------------
n01 | 0.97 | 0.95, 1.00 | < .001
n02 | 0.87 | 0.84, 0.90 | < .001
n03 | 0.69 | 0.67, 0.72 | < .001===================================================================== # Pairwise comparisons
NumBack | Contrast | 95% CI | p
----------------------------------------
n01-n02 | 0.10 | 0.08, 0.13 | < .001
n01-n03 | 0.28 | 0.26, 0.30 | < .001
n02-n03 | 0.18 | 0.15, 0.20 | < .001Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of KSS
Code
fit.Sensi: [df2] Sensi ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of Sensi
KSS | Predicted | 95% CI
----------------------------
1 | 0.88 | 0.83, 0.93
2 | 0.87 | 0.83, 0.91
3 | 0.86 | 0.83, 0.89
4 | 0.85 | 0.82, 0.87
5 | 0.84 | 0.81, 0.87
6 | 0.83 | 0.79, 0.86
7 | 0.82 | 0.77, 0.86
8 | 0.81 | 0.75, 0.86===================================================================== # (Average) Linear trend for KSS
Slope | 95% CI | p
---------------------------
-0.01 | -0.02, 0.00 | 0.135===================================================================== # (Average) Linear trend for KSS
Slope | 95% CI | p
---------------------------
-0.01 | -0.02, 0.00 | 0.135Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Gender
Code
fit.Sensi: [df2] Sensi ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of Sensi
Gender | Predicted | 95% CI
-------------------------------
f | 0.82 | 0.79, 0.86
m | 0.88 | 0.84, 0.91===================================================================== Gender | Predicted | 95% CI | p
----------------------------------------
f | 0.82 | 0.79, 0.86 | < .001
m | 0.88 | 0.84, 0.91 | < .001===================================================================== # Pairwise comparisons
Gender | Contrast | 95% CI | p
----------------------------------------
f-m | -0.06 | -0.11, 0.00 | 0.032Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Check Model fit.Speci
fit.Speci: [df2] Speci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Speci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)
Data: df2
Control: control
REML criterion at convergence: -2444.1
Scaled residuals:
Min 1Q Median 3Q Max
-11.1354 -0.3716 0.0852 0.5787 2.6304
Random effects:
Groups Name Variance Std.Dev.
Sub (Intercept) 0.0009384 0.03063
Residual 0.0031065 0.05574
Number of obs: 891, groups: Sub, 51
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1.000223 0.011646 200.519749 85.885 <2e-16 ***
SesEvening -0.004324 0.003781 846.259686 -1.144 0.2531
ChronoEvening -0.001468 0.009631 50.703456 -0.152 0.8795
Run02 0.005612 0.004574 833.284313 1.227 0.2202
Run03 0.008605 0.004574 833.284313 1.881 0.0603 .
ConditIncongruent 0.001576 0.006236 841.690833 0.253 0.8006
NumBackn02 -0.039656 0.004574 833.284313 -8.670 <2e-16 ***
NumBackn03 -0.074635 0.004574 833.284313 -16.318 <2e-16 ***
KSS -0.004583 0.002578 760.574147 -1.777 0.0759 .
Genderm 0.012095 0.009512 47.941183 1.272 0.2097
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---------------------------------------------------------------------
fit.Speci: [df2] Speci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)
# R2 for Mixed Models
Conditional R2: 0.387
Marginal R2: 0.201
---------------------------------------------------------------------
fit.Speci: [df2] Speci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)
# Intraclass Correlation Coefficient
Adjusted ICC: 0.232
Unadjusted ICC: 0.185
---------------------------------------------------------------------
fit.Speci: [df2] Speci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)
# ICC by Group
Group | ICC
-------------
Sub | 0.232
--------------------------------------------------------------------- Effect of Ses
Code
fit.Speci: [df2] Speci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of Speci
Ses | Predicted | 95% CI
--------------------------------
Morning | 0.95 | 0.94, 0.96
Evening | 0.95 | 0.94, 0.96===================================================================== Ses | Predicted | 95% CI | p
-----------------------------------------
Evening | 0.95 | 0.94, 0.96 | < .001
Morning | 0.95 | 0.94, 0.96 | < .001===================================================================== # Pairwise comparisons
Ses | Contrast | 95% CI | p
-------------------------------------------------
Evening-Morning | -4.32e-03 | -0.01, 0.00 | 0.253Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Chrono
Code
fit.Speci: [df2] Speci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of Speci
Chrono | Predicted | 95% CI
--------------------------------
Morning | 0.95 | 0.94, 0.96
Evening | 0.95 | 0.94, 0.96===================================================================== Chrono | Predicted | 95% CI | p
-----------------------------------------
Evening | 0.95 | 0.94, 0.96 | < .001
Morning | 0.95 | 0.94, 0.96 | < .001===================================================================== # Pairwise comparisons
Chrono | Contrast | 95% CI | p
-------------------------------------------------
Evening-Morning | -1.47e-03 | -0.02, 0.02 | 0.879Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Run
Code
fit.Speci: [df2] Speci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of Speci
Run | Predicted | 95% CI
----------------------------
01 | 0.95 | 0.94, 0.96
02 | 0.95 | 0.94, 0.96
03 | 0.95 | 0.94, 0.97===================================================================== Run | Predicted | 95% CI | p
-------------------------------------
01 | 0.95 | 0.94, 0.96 | < .001
02 | 0.95 | 0.94, 0.96 | < .001
03 | 0.95 | 0.94, 0.97 | < .001===================================================================== # Pairwise comparisons
Run | Contrast | 95% CI | p
---------------------------------------
01-02 | -5.61e-03 | -0.01, 0.00 | 0.330
01-03 | -8.60e-03 | -0.02, 0.00 | 0.181
02-03 | -2.99e-03 | -0.01, 0.01 | 0.513Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Condit
Code
fit.Speci: [df2] Speci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of Speci
Condit | Predicted | 95% CI
------------------------------------
Congruent | 0.95 | 0.94, 0.96
Incongruent | 0.95 | 0.94, 0.96===================================================================== Condit | Predicted | 95% CI | p
---------------------------------------------
Congruent | 0.95 | 0.94, 0.96 | < .001
Incongruent | 0.95 | 0.94, 0.96 | < .001===================================================================== # Pairwise comparisons
Condit | Contrast | 95% CI | p
-------------------------------------------------------
Congruent-Incongruent | -1.58e-03 | -0.01, 0.01 | 0.801Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of NumBack
Code
fit.Speci: [df2] Speci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of Speci
NumBack | Predicted | 95% CI
--------------------------------
n01 | 0.99 | 0.98, 1.00
n02 | 0.95 | 0.94, 0.96
n03 | 0.91 | 0.90, 0.92===================================================================== NumBack | Predicted | 95% CI | p
-----------------------------------------
n01 | 0.99 | 0.98, 1.00 | < .001
n02 | 0.95 | 0.94, 0.96 | < .001
n03 | 0.91 | 0.90, 0.92 | < .001===================================================================== # Pairwise comparisons
NumBack | Contrast | 95% CI | p
----------------------------------------
n01-n02 | 0.04 | 0.03, 0.05 | < .001
n01-n03 | 0.07 | 0.07, 0.08 | < .001
n02-n03 | 0.03 | 0.03, 0.04 | < .001Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of KSS
Code
fit.Speci: [df2] Speci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of Speci
KSS | Predicted | 95% CI
----------------------------
1 | 0.97 | 0.95, 0.98
2 | 0.96 | 0.95, 0.98
3 | 0.96 | 0.95, 0.97
4 | 0.95 | 0.94, 0.96
5 | 0.95 | 0.94, 0.96
6 | 0.94 | 0.93, 0.96
7 | 0.94 | 0.92, 0.95
8 | 0.93 | 0.91, 0.95===================================================================== # (Average) Linear trend for KSS
Slope | 95% CI | p
-------------------------------
-4.58e-03 | -0.01, 0.00 | 0.076===================================================================== # (Average) Linear trend for KSS
Slope | 95% CI | p
-------------------------------
-4.58e-03 | -0.01, 0.00 | 0.076Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Gender
Code
fit.Speci: [df2] Speci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of Speci
Gender | Predicted | 95% CI
-------------------------------
f | 0.95 | 0.93, 0.96
m | 0.96 | 0.94, 0.97===================================================================== Gender | Predicted | 95% CI | p
----------------------------------------
f | 0.95 | 0.93, 0.96 | < .001
m | 0.96 | 0.94, 0.97 | < .001===================================================================== # Pairwise comparisons
Gender | Contrast | 95% CI | p
---------------------------------------
f-m | -0.01 | -0.03, 0.01 | 0.204Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Check Model fit.Preci
fit.Preci: [df2] Preci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Preci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)
Data: df2
Control: control
REML criterion at convergence: -903.7
Scaled residuals:
Min 1Q Median 3Q Max
-4.6505 -0.4965 0.0325 0.6102 2.8078
Random effects:
Groups Name Variance Std.Dev.
Sub (Intercept) 0.005836 0.0764
Residual 0.017732 0.1332
Number of obs: 889, groups: Sub, 51
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.999561 0.028312 191.411034 35.305 <2e-16 ***
SesEvening -0.004639 0.009048 843.623958 -0.513 0.6083
ChronoEvening -0.011303 0.023841 50.340107 -0.474 0.6375
Run02 0.019549 0.010937 831.034887 1.787 0.0742 .
Run03 0.022288 0.010946 831.008107 2.036 0.0420 *
ConditIncongruent 0.001412 0.014984 848.317176 0.094 0.9250
NumBackn02 -0.113440 0.010927 831.008107 -10.381 <2e-16 ***
NumBackn03 -0.232108 0.010948 831.114979 -21.201 <2e-16 ***
KSS -0.010776 0.006196 778.171838 -1.739 0.0824 .
Genderm 0.037181 0.023564 47.677332 1.578 0.1212
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---------------------------------------------------------------------
fit.Preci: [df2] Preci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)
# R2 for Mixed Models
Conditional R2: 0.467
Marginal R2: 0.291
---------------------------------------------------------------------
fit.Preci: [df2] Preci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)
# Intraclass Correlation Coefficient
Adjusted ICC: 0.248
Unadjusted ICC: 0.176
---------------------------------------------------------------------
fit.Preci: [df2] Preci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)
# ICC by Group
Group | ICC
-------------
Sub | 0.248
--------------------------------------------------------------------- Effect of Ses
Code
fit.Preci: [df2] Preci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of Preci
Ses | Predicted | 95% CI
--------------------------------
Morning | 0.86 | 0.84, 0.89
Evening | 0.86 | 0.84, 0.88===================================================================== Ses | Predicted | 95% CI | p
-----------------------------------------
Evening | 0.86 | 0.84, 0.88 | < .001
Morning | 0.86 | 0.84, 0.89 | < .001===================================================================== # Pairwise comparisons
Ses | Contrast | 95% CI | p
-------------------------------------------------
Evening-Morning | -4.64e-03 | -0.02, 0.01 | 0.608Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Chrono
Code
fit.Preci: [df2] Preci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of Preci
Chrono | Predicted | 95% CI
--------------------------------
Morning | 0.87 | 0.84, 0.90
Evening | 0.86 | 0.82, 0.89===================================================================== Chrono | Predicted | 95% CI | p
-----------------------------------------
Evening | 0.86 | 0.82, 0.89 | < .001
Morning | 0.87 | 0.84, 0.90 | < .001===================================================================== # Pairwise comparisons
Chrono | Contrast | 95% CI | p
------------------------------------------------
Evening-Morning | -0.01 | -0.06, 0.04 | 0.636Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Run
Code
fit.Preci: [df2] Preci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of Preci
Run | Predicted | 95% CI
----------------------------
01 | 0.85 | 0.82, 0.87
02 | 0.87 | 0.84, 0.89
03 | 0.87 | 0.84, 0.90===================================================================== Run | Predicted | 95% CI | p
-------------------------------------
01 | 0.85 | 0.82, 0.87 | < .001
02 | 0.87 | 0.84, 0.89 | < .001
03 | 0.87 | 0.84, 0.90 | < .001===================================================================== # Pairwise comparisons
Run | Contrast | 95% CI | p
----------------------------------------
01-02 | -0.02 | -0.04, 0.00 | 0.111
01-03 | -0.02 | -0.04, 0.00 | 0.111
02-03 | -2.74e-03 | -0.02, 0.02 | 0.802Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Condit
Code
fit.Preci: [df2] Preci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of Preci
Condit | Predicted | 95% CI
------------------------------------
Congruent | 0.86 | 0.83, 0.89
Incongruent | 0.86 | 0.84, 0.89===================================================================== Condit | Predicted | 95% CI | p
---------------------------------------------
Congruent | 0.86 | 0.83, 0.89 | < .001
Incongruent | 0.86 | 0.84, 0.89 | < .001===================================================================== # Pairwise comparisons
Condit | Contrast | 95% CI | p
-------------------------------------------------------
Congruent-Incongruent | -1.41e-03 | -0.03, 0.03 | 0.925Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of NumBack
Code
fit.Preci: [df2] Preci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of Preci
NumBack | Predicted | 95% CI
--------------------------------
n01 | 0.98 | 0.95, 1.00
n02 | 0.86 | 0.84, 0.89
n03 | 0.74 | 0.72, 0.77===================================================================== NumBack | Predicted | 95% CI | p
-----------------------------------------
n01 | 0.98 | 0.95, 1.00 | < .001
n02 | 0.86 | 0.84, 0.89 | < .001
n03 | 0.74 | 0.72, 0.77 | < .001===================================================================== # Pairwise comparisons
NumBack | Contrast | 95% CI | p
----------------------------------------
n01-n02 | 0.11 | 0.09, 0.13 | < .001
n01-n03 | 0.23 | 0.21, 0.25 | < .001
n02-n03 | 0.12 | 0.10, 0.14 | < .001Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of KSS
Code
fit.Preci: [df2] Preci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of Preci
KSS | Predicted | 95% CI
----------------------------
1 | 0.90 | 0.85, 0.94
2 | 0.89 | 0.85, 0.92
3 | 0.88 | 0.85, 0.90
4 | 0.86 | 0.84, 0.89
5 | 0.85 | 0.83, 0.88
6 | 0.84 | 0.81, 0.87
7 | 0.83 | 0.79, 0.87
8 | 0.82 | 0.77, 0.87===================================================================== # (Average) Linear trend for KSS
Slope | 95% CI | p
---------------------------
-0.01 | -0.02, 0.00 | 0.082===================================================================== # (Average) Linear trend for KSS
Slope | 95% CI | p
---------------------------
-0.01 | -0.02, 0.00 | 0.082Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Gender
Code
fit.Preci: [df2] Preci ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of Preci
Gender | Predicted | 95% CI
-------------------------------
f | 0.85 | 0.81, 0.88
m | 0.88 | 0.85, 0.92===================================================================== Gender | Predicted | 95% CI | p
----------------------------------------
f | 0.85 | 0.81, 0.88 | < .001
m | 0.88 | 0.85, 0.92 | < .001===================================================================== # Pairwise comparisons
Gender | Contrast | 95% CI | p
---------------------------------------
f-m | -0.04 | -0.08, 0.01 | 0.115Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Check Model fit.FPR
fit.FPR: [df2] FPR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: FPR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)
Data: df2
Control: control
REML criterion at convergence: -2444.1
Scaled residuals:
Min 1Q Median 3Q Max
-2.6304 -0.5787 -0.0852 0.3716 11.1354
Random effects:
Groups Name Variance Std.Dev.
Sub (Intercept) 0.0009384 0.03063
Residual 0.0031065 0.05574
Number of obs: 891, groups: Sub, 51
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -2.235e-04 1.165e-02 2.005e+02 -0.019 0.9847
SesEvening 4.324e-03 3.781e-03 8.463e+02 1.144 0.2531
ChronoEvening 1.468e-03 9.631e-03 5.070e+01 0.152 0.8795
Run02 -5.612e-03 4.574e-03 8.333e+02 -1.227 0.2202
Run03 -8.605e-03 4.574e-03 8.333e+02 -1.881 0.0603 .
ConditIncongruent -1.576e-03 6.236e-03 8.417e+02 -0.253 0.8006
NumBackn02 3.966e-02 4.574e-03 8.333e+02 8.670 <2e-16 ***
NumBackn03 7.464e-02 4.574e-03 8.333e+02 16.318 <2e-16 ***
KSS 4.583e-03 2.578e-03 7.606e+02 1.777 0.0759 .
Genderm -1.209e-02 9.512e-03 4.794e+01 -1.272 0.2097
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---------------------------------------------------------------------
fit.FPR: [df2] FPR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)
# R2 for Mixed Models
Conditional R2: 0.387
Marginal R2: 0.201
---------------------------------------------------------------------
fit.FPR: [df2] FPR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)
# Intraclass Correlation Coefficient
Adjusted ICC: 0.232
Unadjusted ICC: 0.185
---------------------------------------------------------------------
fit.FPR: [df2] FPR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)
# ICC by Group
Group | ICC
-------------
Sub | 0.232
--------------------------------------------------------------------- Effect of Ses
Code
fit.FPR: [df2] FPR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of FPR
Ses | Predicted | 95% CI
--------------------------------
Morning | 0.05 | 0.04, 0.06
Evening | 0.05 | 0.04, 0.06===================================================================== Ses | Predicted | 95% CI | p
-----------------------------------------
Evening | 0.05 | 0.04, 0.06 | < .001
Morning | 0.05 | 0.04, 0.06 | < .001===================================================================== # Pairwise comparisons
Ses | Contrast | 95% CI | p
------------------------------------------------
Evening-Morning | 4.32e-03 | 0.00, 0.01 | 0.253Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Chrono
Code
fit.FPR: [df2] FPR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of FPR
Chrono | Predicted | 95% CI
--------------------------------
Morning | 0.05 | 0.04, 0.06
Evening | 0.05 | 0.04, 0.06===================================================================== Chrono | Predicted | 95% CI | p
-----------------------------------------
Evening | 0.05 | 0.04, 0.06 | < .001
Morning | 0.05 | 0.04, 0.06 | < .001===================================================================== # Pairwise comparisons
Chrono | Contrast | 95% CI | p
------------------------------------------------
Evening-Morning | 1.47e-03 | -0.02, 0.02 | 0.879Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Run
Code
fit.FPR: [df2] FPR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of FPR
Run | Predicted | 95% CI
----------------------------
01 | 0.05 | 0.04, 0.06
02 | 0.05 | 0.04, 0.06
03 | 0.05 | 0.03, 0.06===================================================================== Run | Predicted | 95% CI | p
-------------------------------------
01 | 0.05 | 0.04, 0.06 | < .001
02 | 0.05 | 0.04, 0.06 | < .001
03 | 0.05 | 0.03, 0.06 | < .001===================================================================== # Pairwise comparisons
Run | Contrast | 95% CI | p
--------------------------------------
01-02 | 5.61e-03 | 0.00, 0.01 | 0.330
01-03 | 8.60e-03 | 0.00, 0.02 | 0.181
02-03 | 2.99e-03 | -0.01, 0.01 | 0.513Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Condit
Code
fit.FPR: [df2] FPR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of FPR
Condit | Predicted | 95% CI
------------------------------------
Congruent | 0.05 | 0.04, 0.06
Incongruent | 0.05 | 0.04, 0.06===================================================================== Condit | Predicted | 95% CI | p
---------------------------------------------
Congruent | 0.05 | 0.04, 0.06 | < .001
Incongruent | 0.05 | 0.04, 0.06 | < .001===================================================================== # Pairwise comparisons
Condit | Contrast | 95% CI | p
------------------------------------------------------
Congruent-Incongruent | 1.58e-03 | -0.01, 0.01 | 0.801Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of NumBack
Code
fit.FPR: [df2] FPR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of FPR
NumBack | Predicted | 95% CI
--------------------------------
n01 | 0.01 | 0.00, 0.02
n02 | 0.05 | 0.04, 0.06
n03 | 0.09 | 0.08, 0.10===================================================================== NumBack | Predicted | 95% CI | p
-----------------------------------------
n01 | 0.01 | 0.00, 0.02 | 0.037
n02 | 0.05 | 0.04, 0.06 | < .001
n03 | 0.09 | 0.08, 0.10 | < .001===================================================================== # Pairwise comparisons
NumBack | Contrast | 95% CI | p
------------------------------------------
n01-n02 | -0.04 | -0.05, -0.03 | < .001
n01-n03 | -0.07 | -0.08, -0.07 | < .001
n02-n03 | -0.03 | -0.04, -0.03 | < .001Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of KSS
Code
fit.FPR: [df2] FPR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of FPR
KSS | Predicted | 95% CI
----------------------------
1 | 0.03 | 0.02, 0.05
2 | 0.04 | 0.02, 0.05
3 | 0.04 | 0.03, 0.05
4 | 0.05 | 0.04, 0.06
5 | 0.05 | 0.04, 0.06
6 | 0.06 | 0.04, 0.07
7 | 0.06 | 0.05, 0.08
8 | 0.07 | 0.05, 0.09===================================================================== # (Average) Linear trend for KSS
Slope | 95% CI | p
------------------------------
4.58e-03 | 0.00, 0.01 | 0.076===================================================================== # (Average) Linear trend for KSS
Slope | 95% CI | p
------------------------------
4.58e-03 | 0.00, 0.01 | 0.076Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Gender
Code
fit.FPR: [df2] FPR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of FPR
Gender | Predicted | 95% CI
-------------------------------
f | 0.05 | 0.04, 0.07
m | 0.04 | 0.03, 0.06===================================================================== Gender | Predicted | 95% CI | p
----------------------------------------
f | 0.05 | 0.04, 0.07 | < .001
m | 0.04 | 0.03, 0.06 | < .001===================================================================== # Pairwise comparisons
Gender | Contrast | 95% CI | p
---------------------------------------
f-m | 0.01 | -0.01, 0.03 | 0.204Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Check Model fit.FNR
fit.FNR: [df2] FNR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: FNR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)
Data: df2
Control: control
REML criterion at convergence: -704.5
Scaled residuals:
Min 1Q Median 3Q Max
-2.5049 -0.6101 -0.0791 0.4992 3.6616
Random effects:
Groups Name Variance Std.Dev.
Sub (Intercept) 0.006864 0.08285
Residual 0.022360 0.14953
Number of obs: 891, groups: Sub, 51
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.021180 0.031344 200.304268 0.676 0.50000
SesEvening -0.001712 0.010144 846.426854 -0.169 0.86597
ChronoEvening 0.013169 0.026008 51.086968 0.506 0.61478
Run02 -0.034231 0.012271 833.660412 -2.790 0.00540 **
Run03 -0.032548 0.012271 833.660412 -2.652 0.00814 **
ConditIncongruent 0.005493 0.016743 843.615673 0.328 0.74293
NumBackn02 0.101010 0.012271 833.660412 8.232 7.09e-16 ***
NumBackn03 0.280022 0.012271 833.660412 22.820 < 2e-16 ***
KSS 0.010352 0.006924 765.128571 1.495 0.13533
Genderm -0.055245 0.025690 48.327741 -2.150 0.03655 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---------------------------------------------------------------------
fit.FNR: [df2] FNR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)
# R2 for Mixed Models
Conditional R2: 0.491
Marginal R2: 0.335
---------------------------------------------------------------------
fit.FNR: [df2] FNR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)
# Intraclass Correlation Coefficient
Adjusted ICC: 0.235
Unadjusted ICC: 0.156
---------------------------------------------------------------------
fit.FNR: [df2] FNR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)
# ICC by Group
Group | ICC
-------------
Sub | 0.235
--------------------------------------------------------------------- Effect of Ses
Code
fit.FNR: [df2] FNR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of FNR
Ses | Predicted | 95% CI
--------------------------------
Morning | 0.15 | 0.13, 0.18
Evening | 0.15 | 0.13, 0.18===================================================================== Ses | Predicted | 95% CI | p
-----------------------------------------
Evening | 0.15 | 0.13, 0.18 | < .001
Morning | 0.15 | 0.13, 0.18 | < .001===================================================================== # Pairwise comparisons
Ses | Contrast | 95% CI | p
-------------------------------------------------
Evening-Morning | -1.71e-03 | -0.02, 0.02 | 0.866Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Chrono
Code
fit.FNR: [df2] FNR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of FNR
Chrono | Predicted | 95% CI
--------------------------------
Morning | 0.15 | 0.11, 0.18
Evening | 0.16 | 0.12, 0.20===================================================================== Chrono | Predicted | 95% CI | p
-----------------------------------------
Evening | 0.16 | 0.12, 0.20 | < .001
Morning | 0.15 | 0.11, 0.18 | < .001===================================================================== # Pairwise comparisons
Chrono | Contrast | 95% CI | p
------------------------------------------------
Evening-Morning | 0.01 | -0.04, 0.06 | 0.613Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Run
Code
fit.FNR: [df2] FNR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of FNR
Run | Predicted | 95% CI
----------------------------
01 | 0.18 | 0.15, 0.20
02 | 0.14 | 0.11, 0.17
03 | 0.14 | 0.11, 0.17===================================================================== Run | Predicted | 95% CI | p
-------------------------------------
01 | 0.18 | 0.15, 0.20 | < .001
02 | 0.14 | 0.11, 0.17 | < .001
03 | 0.14 | 0.11, 0.17 | < .001===================================================================== # Pairwise comparisons
Run | Contrast | 95% CI | p
---------------------------------------
01-02 | 0.03 | 0.01, 0.06 | 0.012
01-03 | 0.03 | 0.01, 0.06 | 0.012
02-03 | -1.68e-03 | -0.03, 0.02 | 0.891Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Condit
Code
fit.FNR: [df2] FNR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of FNR
Condit | Predicted | 95% CI
------------------------------------
Congruent | 0.15 | 0.12, 0.18
Incongruent | 0.16 | 0.13, 0.19===================================================================== Condit | Predicted | 95% CI | p
---------------------------------------------
Congruent | 0.15 | 0.12, 0.18 | < .001
Incongruent | 0.16 | 0.13, 0.19 | < .001===================================================================== # Pairwise comparisons
Condit | Contrast | 95% CI | p
-------------------------------------------------------
Congruent-Incongruent | -5.49e-03 | -0.04, 0.03 | 0.743Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of NumBack
Code
fit.FNR: [df2] FNR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of FNR
NumBack | Predicted | 95% CI
---------------------------------
n01 | 0.03 | 0.00, 0.05
n02 | 0.13 | 0.10, 0.16
n03 | 0.31 | 0.28, 0.33===================================================================== NumBack | Predicted | 95% CI | p
------------------------------------------
n01 | 0.03 | 0.00, 0.05 | 0.069
n02 | 0.13 | 0.10, 0.16 | < .001
n03 | 0.31 | 0.28, 0.33 | < .001===================================================================== # Pairwise comparisons
NumBack | Contrast | 95% CI | p
------------------------------------------
n01-n02 | -0.10 | -0.13, -0.08 | < .001
n01-n03 | -0.28 | -0.30, -0.26 | < .001
n02-n03 | -0.18 | -0.20, -0.15 | < .001Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of KSS
Code
fit.FNR: [df2] FNR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of FNR
KSS | Predicted | 95% CI
----------------------------
1 | 0.12 | 0.07, 0.17
2 | 0.13 | 0.09, 0.17
3 | 0.14 | 0.11, 0.17
4 | 0.15 | 0.13, 0.18
5 | 0.16 | 0.13, 0.19
6 | 0.17 | 0.14, 0.21
7 | 0.18 | 0.14, 0.23
8 | 0.19 | 0.14, 0.25===================================================================== # (Average) Linear trend for KSS
Slope | 95% CI | p
---------------------------
0.01 | 0.00, 0.02 | 0.135===================================================================== # (Average) Linear trend for KSS
Slope | 95% CI | p
---------------------------
0.01 | 0.00, 0.02 | 0.135Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Gender
Code
fit.FNR: [df2] FNR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of FNR
Gender | Predicted | 95% CI
-------------------------------
f | 0.18 | 0.14, 0.21
m | 0.12 | 0.09, 0.16===================================================================== Gender | Predicted | 95% CI | p
----------------------------------------
f | 0.18 | 0.14, 0.21 | < .001
m | 0.12 | 0.09, 0.16 | < .001===================================================================== # Pairwise comparisons
Gender | Contrast | 95% CI | p
--------------------------------------
f-m | 0.06 | 0.00, 0.11 | 0.032Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Check Model fit.FDR
fit.FDR: [df2] FDR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: FDR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)
Data: df2
Control: control
REML criterion at convergence: -903.7
Scaled residuals:
Min 1Q Median 3Q Max
-2.8078 -0.6102 -0.0325 0.4965 4.6505
Random effects:
Groups Name Variance Std.Dev.
Sub (Intercept) 0.005836 0.0764
Residual 0.017732 0.1332
Number of obs: 889, groups: Sub, 51
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 4.386e-04 2.831e-02 1.914e+02 0.015 0.9877
SesEvening 4.639e-03 9.048e-03 8.436e+02 0.513 0.6083
ChronoEvening 1.130e-02 2.384e-02 5.034e+01 0.474 0.6375
Run02 -1.955e-02 1.094e-02 8.310e+02 -1.787 0.0742 .
Run03 -2.229e-02 1.095e-02 8.310e+02 -2.036 0.0420 *
ConditIncongruent -1.412e-03 1.498e-02 8.483e+02 -0.094 0.9250
NumBackn02 1.134e-01 1.093e-02 8.310e+02 10.381 <2e-16 ***
NumBackn03 2.321e-01 1.095e-02 8.311e+02 21.201 <2e-16 ***
KSS 1.078e-02 6.196e-03 7.782e+02 1.739 0.0824 .
Genderm -3.718e-02 2.356e-02 4.768e+01 -1.578 0.1212
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---------------------------------------------------------------------
fit.FDR: [df2] FDR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)
# R2 for Mixed Models
Conditional R2: 0.467
Marginal R2: 0.291
---------------------------------------------------------------------
fit.FDR: [df2] FDR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)
# Intraclass Correlation Coefficient
Adjusted ICC: 0.248
Unadjusted ICC: 0.176
---------------------------------------------------------------------
fit.FDR: [df2] FDR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)
# ICC by Group
Group | ICC
-------------
Sub | 0.248
--------------------------------------------------------------------- Effect of Ses
Code
fit.FDR: [df2] FDR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of FDR
Ses | Predicted | 95% CI
--------------------------------
Morning | 0.14 | 0.11, 0.16
Evening | 0.14 | 0.12, 0.16===================================================================== Ses | Predicted | 95% CI | p
-----------------------------------------
Evening | 0.14 | 0.12, 0.16 | < .001
Morning | 0.14 | 0.11, 0.16 | < .001===================================================================== # Pairwise comparisons
Ses | Contrast | 95% CI | p
------------------------------------------------
Evening-Morning | 4.64e-03 | -0.01, 0.02 | 0.608Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Chrono
Code
fit.FDR: [df2] FDR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of FDR
Chrono | Predicted | 95% CI
--------------------------------
Morning | 0.13 | 0.10, 0.16
Evening | 0.14 | 0.11, 0.18===================================================================== Chrono | Predicted | 95% CI | p
-----------------------------------------
Evening | 0.14 | 0.11, 0.18 | < .001
Morning | 0.13 | 0.10, 0.16 | < .001===================================================================== # Pairwise comparisons
Chrono | Contrast | 95% CI | p
------------------------------------------------
Evening-Morning | 0.01 | -0.04, 0.06 | 0.636Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Run
Code
fit.FDR: [df2] FDR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of FDR
Run | Predicted | 95% CI
----------------------------
01 | 0.15 | 0.13, 0.18
02 | 0.13 | 0.11, 0.16
03 | 0.13 | 0.10, 0.16===================================================================== Run | Predicted | 95% CI | p
-------------------------------------
01 | 0.15 | 0.13, 0.18 | < .001
02 | 0.13 | 0.11, 0.16 | < .001
03 | 0.13 | 0.10, 0.16 | < .001===================================================================== # Pairwise comparisons
Run | Contrast | 95% CI | p
--------------------------------------
01-02 | 0.02 | 0.00, 0.04 | 0.111
01-03 | 0.02 | 0.00, 0.04 | 0.111
02-03 | 2.74e-03 | -0.02, 0.02 | 0.802Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Condit
Code
fit.FDR: [df2] FDR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of FDR
Condit | Predicted | 95% CI
------------------------------------
Congruent | 0.14 | 0.11, 0.17
Incongruent | 0.14 | 0.11, 0.16===================================================================== Condit | Predicted | 95% CI | p
---------------------------------------------
Congruent | 0.14 | 0.11, 0.17 | < .001
Incongruent | 0.14 | 0.11, 0.16 | < .001===================================================================== # Pairwise comparisons
Condit | Contrast | 95% CI | p
------------------------------------------------------
Congruent-Incongruent | 1.41e-03 | -0.03, 0.03 | 0.925Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of NumBack
Code
fit.FDR: [df2] FDR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of FDR
NumBack | Predicted | 95% CI
---------------------------------
n01 | 0.02 | 0.00, 0.05
n02 | 0.14 | 0.11, 0.16
n03 | 0.26 | 0.23, 0.28===================================================================== NumBack | Predicted | 95% CI | p
------------------------------------------
n01 | 0.02 | 0.00, 0.05 | 0.082
n02 | 0.14 | 0.11, 0.16 | < .001
n03 | 0.26 | 0.23, 0.28 | < .001===================================================================== # Pairwise comparisons
NumBack | Contrast | 95% CI | p
------------------------------------------
n01-n02 | -0.11 | -0.13, -0.09 | < .001
n01-n03 | -0.23 | -0.25, -0.21 | < .001
n02-n03 | -0.12 | -0.14, -0.10 | < .001Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of KSS
Code
fit.FDR: [df2] FDR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of FDR
KSS | Predicted | 95% CI
----------------------------
1 | 0.10 | 0.06, 0.15
2 | 0.11 | 0.08, 0.15
3 | 0.12 | 0.10, 0.15
4 | 0.14 | 0.11, 0.16
5 | 0.15 | 0.12, 0.17
6 | 0.16 | 0.13, 0.19
7 | 0.17 | 0.13, 0.21
8 | 0.18 | 0.13, 0.23===================================================================== # (Average) Linear trend for KSS
Slope | 95% CI | p
---------------------------
0.01 | 0.00, 0.02 | 0.082===================================================================== # (Average) Linear trend for KSS
Slope | 95% CI | p
---------------------------
0.01 | 0.00, 0.02 | 0.082Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Gender
Code
fit.FDR: [df2] FDR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of FDR
Gender | Predicted | 95% CI
-------------------------------
f | 0.15 | 0.12, 0.19
m | 0.12 | 0.08, 0.15===================================================================== Gender | Predicted | 95% CI | p
----------------------------------------
f | 0.15 | 0.12, 0.19 | < .001
m | 0.12 | 0.08, 0.15 | < .001===================================================================== # Pairwise comparisons
Gender | Contrast | 95% CI | p
---------------------------------------
f-m | 0.04 | -0.01, 0.08 | 0.115Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Check Model fit.NPV
fit.NPV: [df2] NPV ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: NPV ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)
Data: df2
Control: control
REML criterion at convergence: -2775.2
Scaled residuals:
Min 1Q Median 3Q Max
-3.3747 -0.5254 0.0772 0.6310 2.6182
Random effects:
Groups Name Variance Std.Dev.
Sub (Intercept) 0.0006779 0.02604
Residual 0.0021282 0.04613
Number of obs: 891, groups: Sub, 51
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 9.928e-01 9.742e-03 1.966e+02 101.907 < 2e-16 ***
SesEvening -1.615e-04 3.130e-03 8.461e+02 -0.052 0.95885
ChronoEvening -4.273e-03 8.146e-03 5.099e+01 -0.525 0.60219
Run02 1.030e-02 3.786e-03 8.336e+02 2.721 0.00664 **
Run03 1.044e-02 3.786e-03 8.336e+02 2.757 0.00595 **
ConditIncongruent -1.883e-03 5.174e-03 8.472e+02 -0.364 0.71592
NumBackn02 -3.229e-02 3.786e-03 8.336e+02 -8.530 < 2e-16 ***
NumBackn03 -8.851e-02 3.786e-03 8.336e+02 -23.381 < 2e-16 ***
KSS -3.139e-03 2.141e-03 7.734e+02 -1.466 0.14299
Genderm 1.773e-02 8.050e-03 4.829e+01 2.202 0.03249 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---------------------------------------------------------------------
fit.NPV: [df2] NPV ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)
# R2 for Mixed Models
Conditional R2: 0.502
Marginal R2: 0.344
---------------------------------------------------------------------
fit.NPV: [df2] NPV ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)
# Intraclass Correlation Coefficient
Adjusted ICC: 0.242
Unadjusted ICC: 0.159
---------------------------------------------------------------------
fit.NPV: [df2] NPV ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)
# ICC by Group
Group | ICC
-------------
Sub | 0.242
--------------------------------------------------------------------- Effect of Ses
Code
fit.NPV: [df2] NPV ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of NPV
Ses | Predicted | 95% CI
--------------------------------
Morning | 0.95 | 0.94, 0.96
Evening | 0.95 | 0.94, 0.96===================================================================== Ses | Predicted | 95% CI | p
-----------------------------------------
Evening | 0.95 | 0.94, 0.96 | < .001
Morning | 0.95 | 0.94, 0.96 | < .001===================================================================== # Pairwise comparisons
Ses | Contrast | 95% CI | p
-------------------------------------------------
Evening-Morning | -1.62e-04 | -0.01, 0.01 | 0.959Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Chrono
Code
fit.NPV: [df2] NPV ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of NPV
Chrono | Predicted | 95% CI
--------------------------------
Morning | 0.95 | 0.94, 0.96
Evening | 0.95 | 0.94, 0.96===================================================================== Chrono | Predicted | 95% CI | p
-----------------------------------------
Evening | 0.95 | 0.94, 0.96 | < .001
Morning | 0.95 | 0.94, 0.96 | < .001===================================================================== # Pairwise comparisons
Chrono | Contrast | 95% CI | p
-------------------------------------------------
Evening-Morning | -4.27e-03 | -0.02, 0.01 | 0.600Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Run
Code
fit.NPV: [df2] NPV ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of NPV
Run | Predicted | 95% CI
----------------------------
01 | 0.94 | 0.94, 0.95
02 | 0.95 | 0.95, 0.96
03 | 0.95 | 0.95, 0.96===================================================================== Run | Predicted | 95% CI | p
-------------------------------------
01 | 0.94 | 0.94, 0.95 | < .001
02 | 0.95 | 0.95, 0.96 | < .001
03 | 0.95 | 0.95, 0.96 | < .001===================================================================== # Pairwise comparisons
Run | Contrast | 95% CI | p
----------------------------------------
01-02 | -0.01 | -0.02, 0.00 | 0.010
01-03 | -0.01 | -0.02, 0.00 | 0.010
02-03 | -1.37e-04 | -0.01, 0.01 | 0.971Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Condit
Code
fit.NPV: [df2] NPV ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of NPV
Condit | Predicted | 95% CI
------------------------------------
Congruent | 0.95 | 0.94, 0.96
Incongruent | 0.95 | 0.94, 0.96===================================================================== Condit | Predicted | 95% CI | p
---------------------------------------------
Congruent | 0.95 | 0.94, 0.96 | < .001
Incongruent | 0.95 | 0.94, 0.96 | < .001===================================================================== # Pairwise comparisons
Condit | Contrast | 95% CI | p
------------------------------------------------------
Congruent-Incongruent | 1.88e-03 | -0.01, 0.01 | 0.716Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of NumBack
Code
fit.NPV: [df2] NPV ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of NPV
NumBack | Predicted | 95% CI
--------------------------------
n01 | 0.99 | 0.98, 1.00
n02 | 0.96 | 0.95, 0.97
n03 | 0.90 | 0.89, 0.91===================================================================== NumBack | Predicted | 95% CI | p
-----------------------------------------
n01 | 0.99 | 0.98, 1.00 | < .001
n02 | 0.96 | 0.95, 0.97 | < .001
n03 | 0.90 | 0.89, 0.91 | < .001===================================================================== # Pairwise comparisons
NumBack | Contrast | 95% CI | p
----------------------------------------
n01-n02 | 0.03 | 0.02, 0.04 | < .001
n01-n03 | 0.09 | 0.08, 0.10 | < .001
n02-n03 | 0.06 | 0.05, 0.06 | < .001Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of KSS
Code
fit.NPV: [df2] NPV ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of NPV
KSS | Predicted | 95% CI
----------------------------
1 | 0.96 | 0.95, 0.98
2 | 0.96 | 0.95, 0.97
3 | 0.95 | 0.95, 0.96
4 | 0.95 | 0.94, 0.96
5 | 0.95 | 0.94, 0.96
6 | 0.95 | 0.93, 0.96
7 | 0.94 | 0.93, 0.96
8 | 0.94 | 0.92, 0.96===================================================================== # (Average) Linear trend for KSS
Slope | 95% CI | p
-------------------------------
-3.14e-03 | -0.01, 0.00 | 0.143===================================================================== # (Average) Linear trend for KSS
Slope | 95% CI | p
-------------------------------
-3.14e-03 | -0.01, 0.00 | 0.143Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Gender
Code
fit.NPV: [df2] NPV ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of NPV
Gender | Predicted | 95% CI
-------------------------------
f | 0.94 | 0.93, 0.95
m | 0.96 | 0.95, 0.97===================================================================== Gender | Predicted | 95% CI | p
----------------------------------------
f | 0.94 | 0.93, 0.95 | < .001
m | 0.96 | 0.95, 0.97 | < .001===================================================================== # Pairwise comparisons
Gender | Contrast | 95% CI | p
----------------------------------------
f-m | -0.02 | -0.03, 0.00 | 0.028Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Check Model fit.FOR
fit.FOR: [df2] FOR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: FOR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)
Data: df2
Control: control
REML criterion at convergence: -2775.2
Scaled residuals:
Min 1Q Median 3Q Max
-2.6182 -0.6310 -0.0772 0.5254 3.3747
Random effects:
Groups Name Variance Std.Dev.
Sub (Intercept) 0.0006779 0.02604
Residual 0.0021282 0.04613
Number of obs: 891, groups: Sub, 51
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 7.191e-03 9.742e-03 1.966e+02 0.738 0.46131
SesEvening 1.615e-04 3.130e-03 8.461e+02 0.052 0.95885
ChronoEvening 4.273e-03 8.146e-03 5.099e+01 0.525 0.60219
Run02 -1.030e-02 3.786e-03 8.336e+02 -2.721 0.00664 **
Run03 -1.044e-02 3.786e-03 8.336e+02 -2.757 0.00595 **
ConditIncongruent 1.883e-03 5.174e-03 8.472e+02 0.364 0.71592
NumBackn02 3.229e-02 3.786e-03 8.336e+02 8.530 < 2e-16 ***
NumBackn03 8.851e-02 3.786e-03 8.336e+02 23.381 < 2e-16 ***
KSS 3.139e-03 2.141e-03 7.734e+02 1.466 0.14299
Genderm -1.773e-02 8.050e-03 4.829e+01 -2.202 0.03249 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---------------------------------------------------------------------
fit.FOR: [df2] FOR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)
# R2 for Mixed Models
Conditional R2: 0.502
Marginal R2: 0.344
---------------------------------------------------------------------
fit.FOR: [df2] FOR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)
# Intraclass Correlation Coefficient
Adjusted ICC: 0.242
Unadjusted ICC: 0.159
---------------------------------------------------------------------
fit.FOR: [df2] FOR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)
# ICC by Group
Group | ICC
-------------
Sub | 0.242
--------------------------------------------------------------------- Effect of Ses
Code
fit.FOR: [df2] FOR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of FOR
Ses | Predicted | 95% CI
--------------------------------
Morning | 0.05 | 0.04, 0.06
Evening | 0.05 | 0.04, 0.06===================================================================== Ses | Predicted | 95% CI | p
-----------------------------------------
Evening | 0.05 | 0.04, 0.06 | < .001
Morning | 0.05 | 0.04, 0.06 | < .001===================================================================== # Pairwise comparisons
Ses | Contrast | 95% CI | p
------------------------------------------------
Evening-Morning | 1.62e-04 | -0.01, 0.01 | 0.959Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Chrono
Code
fit.FOR: [df2] FOR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of FOR
Chrono | Predicted | 95% CI
--------------------------------
Morning | 0.05 | 0.04, 0.06
Evening | 0.05 | 0.04, 0.06===================================================================== Chrono | Predicted | 95% CI | p
-----------------------------------------
Evening | 0.05 | 0.04, 0.06 | < .001
Morning | 0.05 | 0.04, 0.06 | < .001===================================================================== # Pairwise comparisons
Chrono | Contrast | 95% CI | p
------------------------------------------------
Evening-Morning | 4.27e-03 | -0.01, 0.02 | 0.600Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Run
Code
fit.FOR: [df2] FOR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of FOR
Run | Predicted | 95% CI
----------------------------
01 | 0.06 | 0.05, 0.06
02 | 0.05 | 0.04, 0.05
03 | 0.05 | 0.04, 0.05===================================================================== Run | Predicted | 95% CI | p
-------------------------------------
01 | 0.06 | 0.05, 0.06 | < .001
02 | 0.05 | 0.04, 0.05 | < .001
03 | 0.05 | 0.04, 0.05 | < .001===================================================================== # Pairwise comparisons
Run | Contrast | 95% CI | p
--------------------------------------
01-02 | 0.01 | 0.00, 0.02 | 0.010
01-03 | 0.01 | 0.00, 0.02 | 0.010
02-03 | 1.37e-04 | -0.01, 0.01 | 0.971Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Condit
Code
fit.FOR: [df2] FOR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of FOR
Condit | Predicted | 95% CI
------------------------------------
Congruent | 0.05 | 0.04, 0.06
Incongruent | 0.05 | 0.04, 0.06===================================================================== Condit | Predicted | 95% CI | p
---------------------------------------------
Congruent | 0.05 | 0.04, 0.06 | < .001
Incongruent | 0.05 | 0.04, 0.06 | < .001===================================================================== # Pairwise comparisons
Condit | Contrast | 95% CI | p
-------------------------------------------------------
Congruent-Incongruent | -1.88e-03 | -0.01, 0.01 | 0.716Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of NumBack
Code
fit.FOR: [df2] FOR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of FOR
NumBack | Predicted | 95% CI
---------------------------------
n01 | 0.01 | 0.00, 0.02
n02 | 0.04 | 0.03, 0.05
n03 | 0.10 | 0.09, 0.11===================================================================== NumBack | Predicted | 95% CI | p
------------------------------------------
n01 | 8.76e-03 | 0.00, 0.02 | 0.054
n02 | 0.04 | 0.03, 0.05 | < .001
n03 | 0.10 | 0.09, 0.11 | < .001===================================================================== # Pairwise comparisons
NumBack | Contrast | 95% CI | p
------------------------------------------
n01-n02 | -0.03 | -0.04, -0.02 | < .001
n01-n03 | -0.09 | -0.10, -0.08 | < .001
n02-n03 | -0.06 | -0.06, -0.05 | < .001Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of KSS
Code
fit.FOR: [df2] FOR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of FOR
KSS | Predicted | 95% CI
----------------------------
1 | 0.04 | 0.02, 0.05
2 | 0.04 | 0.03, 0.05
3 | 0.05 | 0.04, 0.05
4 | 0.05 | 0.04, 0.06
5 | 0.05 | 0.04, 0.06
6 | 0.05 | 0.04, 0.07
7 | 0.06 | 0.04, 0.07
8 | 0.06 | 0.04, 0.08===================================================================== # (Average) Linear trend for KSS
Slope | 95% CI | p
------------------------------
3.14e-03 | 0.00, 0.01 | 0.143===================================================================== # (Average) Linear trend for KSS
Slope | 95% CI | p
------------------------------
3.14e-03 | 0.00, 0.01 | 0.143Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
Effect of Gender
Code
fit.FOR: [df2] FOR ~ Ses + Chrono + Run + Condit + NumBack + KSS + Gender +
(1 | Sub)===================================================================== # Average predicted values of FOR
Gender | Predicted | 95% CI
-------------------------------
f | 0.06 | 0.05, 0.07
m | 0.04 | 0.03, 0.05===================================================================== Gender | Predicted | 95% CI | p
----------------------------------------
f | 0.06 | 0.05, 0.07 | < .001
m | 0.04 | 0.03, 0.05 | < .001===================================================================== # Pairwise comparisons
Gender | Contrast | 95% CI | p
--------------------------------------
f-m | 0.02 | 0.00, 0.03 | 0.028Plot Effect
Code
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 # + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88, width=16, height=48)
gg88
[1] "cat0" "control" "custom_palette" "df0"
[5] "df2" "extra" "fbase" "fit.Accur"
[9] "fit.FDR" "fit.FN" "fit.FNR" "fit.FOR"
[13] "fit.FP" "fit.FPR" "fit.NPV" "fit.Preci"
[17] "fit.RT" "fit.Sensi" "fit.Speci" "fit.TN"
[21] "fit.TP" "get_eff_null" "get_model_info" "gg88"
[25] "ggeff" "ifd0" "ifn0" "line0h"
[29] "model" "ofd0" "pp" "REML"
[33] "sep0" "sep1" "sep2" "terms"